2023 MCBK global meeting—Lightning talk abstracts

IF 2.6 Q2 HEALTH POLICY & SERVICES Learning Health Systems Pub Date : 2024-07-06 DOI:10.1002/lrh2.10443

{"title":"2023 MCBK global meeting—Lightning talk abstracts","authors":"","doi":"10.1002/lrh2.10443","DOIUrl":null,"url":null,"abstract":"Muhammad Afzal, School of Computing and Digital Technology, Birmingham City University[email protected]Contemporary scientific communication relies heavily on document-based systems like journal articles, books, and reports for sharing research findings. However, large documents limit opportunities for efficient knowledge dissemination due to limitation in processing of different subsections within a document to understand the meaning of information units. This research aims to develop a smart repository that moves beyond documents and introduces smaller, computable units of knowledge. By assessing biomedical data sources, we will build a repository to make scientific knowledge more representable, computable, and shareable. The rationale is to enhance how researchers communicate and manage information in the rapidly evolving digital era.The work focuses on developing a new repository that goes beyond the document-based paradigm by fusing biomedical and health and life sciences data sources, such as PubMed Central. New protocols and methods will be designed to identify relevant sections in the documents to extract smaller knowledge units. The proposed repository with key features storage, retrieval, representation, and sharing will be optimized for the granular units. Integration strategies with existing platforms like PubMed will be devised. Usability testing will refine the interface to boost engagement. Interoperability mechanisms will ensure compatibility with existing systems.By enabling scientific knowledge to be shared in smaller units, this repository has the potential to revolutionize scientific communication and collaboration. Breaking down information into granular components is expected to create new opportunities for innovation, discovery, and the development of advanced analytics tools. The repository will facilitate efficient access to health evidence, benefiting researchers, clinicians in creating systematic reviewers that require rapid evidence synthesis. Further, the computable units extracted from documents could be modeled into interoperable resources like FHIR, thereby support the Evidence Based Medicine on FHIR (EBMonFHIR) project is extending FHIR to provide a standard for machine-interpretable exchange of scientific knowledge. This would also allow developers to build innovative AI systems for objectives such as diagnostic and treatment support.By reducing the need for manual effort in finding and formatting evidence, the repository will pave the way for automating knowledge synthesis and management and will empower various stakeholders with enhanced efficiency, interoperability, and analytical capabilities to progress research and practice.Miguel Aljibe, University of the Philippines[email protected]Alvin Marcelo, University of the Philippines-Manila[email protected]Janus Ong, University of the Philippines-Manila[email protected]Geohari Hamoy[email protected]The World Health Organization released the SMART Guidelines to advise member countries on a systematic approach to converting narrative policies into machine-readable formats. In the Philippines, a group of researchers attempted to apply the guidelines to the national viral hepatitis program. Several lessons were learned. First, government sponsorship is crucial at the outset as the effort of conversion can be substantial and confidence that the output will eventually be promulgated is essential. Second, consensus building is important to ensure that all stakeholders have been consulted and that the semantics are understood and accepted by all. Third, international standards such as HL7 FHIR and SNOMED play important roles. They must be made available for all to use. Lastly, constant communications among stakeholders (see consensus building in #2) will enable data exchange because of the trust built with open channels.Ravi Bajra Bajracharya, CEO/Knowledge graph engineer, datum.md[email protected]Knowledge graphs are formal representation of knowledge bases as a network of nodes and edges where nodes represent biomedical concepts and edges link related nodes together to encode relationships between concepts in a knowledge base. A semantic knowledge graph treats each node as a resource with a unique URI and a semantic namespace and thereby normalizes concepts to similar semantic space across various sources of knowledge or information.The primitives in our case can be health data standards, ontologies, terminologies, and vocabularies, which can be enriched using both structured and unstructured knowledge bases ranging from basic science, genomics data to clinical longitudinal records and population health demographics to community, environmental and behavioral research data.The use of semantic data representation in a knowledge graph platform has proven to expand the use cases of the represented knowledge base to broader applications in the areas of analytics and prediction promoting reuse and interoperability of underlying biomedical knowledge sources involved.Neha Bhasin, MD, MS, University of San Francisco[email protected]William J. Bosl, Digital Health Informatics Program, University of San Francisco, Center for AI and Medicine, University of San Francisco, Computational Health Informatics Program, Boston Children's Hospital, Harvard Medical School[email protected]Introduction: Psychiatric disorders impose an enormous burden of disease on all populations of the world, a burden that is likely underestimated because of a failure to appreciate the interconnectedness of mental and physical health. While research in basic neuroscience has advanced considerably, translation of new knowledge into new treatments has been slow. Mental disorders generally emerge over time due to complex factors, creating opportunities for early intervention to redirect the developmental trajectories and to monitor the efficacy of treatments. Unfortunately, the data necessary to monitor neurodevelopmental trajectories relevant to psychiatric disorders are typically not collected in routine clinical care. Further, research to develop computational algorithms to model disease trajectories depends on these data that are not generally available from clinical sources. We propose creation of a new paradigm for a learning mental health system that is designed around a globally accessible personal psychiatric database that will support research in computational psychiatry and will evolve into clinical decision support systems.Objective: The primary goal for this research is to create a new paradigm to collect mental health relevant data to develop algorithms for monitoring neurodevelopmental trajectories to enable early risk assessment and monitoring for mental disorders.Methods: Our team and others are developing computational approaches that enable latent information from complex, multimodal data, including EEG as a functional brain measurement, to be extracted. Importantly, clinical validation of these computational methods will require standardized datasets from large, diverse populations. We are implementing an open and secure platform for brain specific data using open (FHIR) APIs that will enable this data to be shared with institutionally based EHRs. Our goal is to create a globally accessible cloud-based personal mental health record for research in computational psychiatry that also seeks to create a brain-health monitoring clinical paradigm. Consent for running algorithms on this data in a federated fashion will be mandatory. Our methodology integrates three components to create a computable mental health knowledge system: a mobile platform to collect EEG and other relevant data outside of the clinic, a FHIR-based personal mental health database, and algorithms to compute neurodevelopmental risk trajectories from the database.Discussion: Our previous research has recruited research cohorts to identify biomarkers for neurodevelopmental disorders in children from EEG recordings. To transition this work from the laboratory to practice, longitudinal data is needed for clinical validation studies. These data are not typically collected in routine pediatric checkups, necessitating a new paradigm for acquiring the relevant data. We are proposing a new kind of learning health system in which the need for ecologically valid research data will drive the creation of a new patient-centric approach to mental health monitoring. Our initial focus will be on neurodevelopmental disorders in children.Conclusion: Care for brain disorders may be advanced by patient-centered longitudinal research that implements Computable Biopsychosocial Knowledge for psychiatry through FHIR-based, brain-specific personal health record systems, together with algorithms tested and developed on that database. An initial pilot implementation for childhood neurodevelopmental disorders is being implemented to demonstrate the system.Peter Boisvert, Department of Learning Health Sciences, University of Michigan[email protected]Marisa Conte, Department of Learning Health Sciences, University of Michigan[email protected]Allen Flynn, Department of Learning Health Sciences, University of Michigan[email protected]Charles P Friedman, Department of Learning Health Sciences, University of Michigan—on behalf of the Knowledge Systems Lab[email protected]Metadata is essential to achieve FAIR (Findable, Accessible, Interoperable and Reusable) computable biomedical knowledge (CBK). The Mobilizing Computable Biomedical Knowledge (MCBK) community has defined categories of metadata[1] and supports ongoing efforts to develop a minimal metadata model for CBK. Complementing this work, we recognize an emerging role for extensible metadata, which can be generated by both human- and non-human actors throughout the lifecycle of a CBK artifact.Technical methods for enabling extensible metadata are well-known, including Protégé for ontology creation and management, and the CEDAR workbench for authoring metadata. Existing domain and infrastructural metadata can capture various perspectives on CBK artifacts or collections, including domain-specific (Human Phenotype Ontology) or provenance (PROV-O) ontologies, technology-specific metadata (Maven coordinates), and so on. In a linked-data world, these should all be linkable and interoperable in the context of a particular instance or class of CBK, and its stakeholders.Trust in CBK is essential for its use, dissemination, and reuse. Trust is also essentially a function of networks and community, not of a CBK artifact itself. Extensibility allows communities to enrich metadata with elements that are essential for trust by that community, increasing the potential for increased implementation and use.Ongoing efforts to develop standards and models for CBK metadata should also include ways to promote extensible metadata. Extensibility acknowledges the plasticity of both CBK and its uses/users, and the enriched metadata provided by diverse communities or other agents may increase the adoption, usefulness, and reusability of CBK.1 Alper BS, Flynn A, Bray BE, Conte ML, Eldredge C, Gold S, et al. Categorizing metadata to help mobilize computable biomedical knowledge. Learn Health Syst 2021;n/a(n/a):e10271.Ruth Carlyle, NHS England[email protected]Application and use of computable biomedical knowledge depends upon digital skills and health literacy, the ability to access, assess and use health information.This lightning talk will share data from England on variations in health literacy and digital connectivity. At a national level, 43% adults aged 16–25 struggle to understand health information that uses words. When numbers are added, 61% adults struggle—as most health information in practice combines words and numbers, this means that the majority of the population struggle even when health information is in print (Rowlands et al 2015). If members of the public are to contribute to and benefit from the personalisation of health information made possible by computable biomedical knowledge, they need to understand the information and to trust both the sources and the usage.The national NHS Knowledge and Library Services team (now part of NHS England) provides strategic leadership for NHS knowledge and library services in England. The aim in its strategy is that ‘NHS bodies, their staff, learners, patients and the public use the right knowledge and evidence, at the right time, in the right place, enabling high quality decision-making, learning, research and innovation, to achieve excellent healthcare and health improvement’ (Knowledge for Healthcare, 2021). As part of delivering this aim, we have a workstream on health literacy and patient information, developing national partnerships and creating and spreading tools.As a tool to understand health literacy, the team commissioned the University of Southampton to reanalyze literacy and numeracy data as geodata, to show local variation. This lightning talk will give a brief insight into both health literacy geodata and mapping of digital connectivity, with its implications for computable biomedical knowledge.The talk will also introduce a national partnership with CILIP (the professional body for librarians and information professionals in the UK), Libraries Connected (overseeing public/community libraries) and Arts Council England. Through the partnership, community-based public libraries and prison libraries have worked with members of the public to increase confidence in health literacy and provide skills and digital hubs to interact with health information in digital forms. Learnings from this activity demonstrate the role that libraries and library staff in health and community settings can provide in increasing the health literacy and digital literacy for the public on which use of computable biomedical knowledge depends.Shuaipu Chen, School of Information Management, Wuhan University, Institute of Big Data, Wuhan University[email protected]Yuxing Qian, School of Information Management, Wuhan University, Institute of Big Data, Wuhan University[email protected]Zhenghao Liu, School of Information Management, Wuhan University, Institute of Big Data, Wuhan University[email protected]In the application of the Data-Information-Knowledge-Wisdom model in biomedical and health sciences, the computation of the value of knowledge becomes pivotal in empowering decision-making as a tangible manifestation of wisdom.We propose a specific method for knowledge computation, drawing from both metaknowledge theory and scenario theory. Metaknowledge theory highlights the value of knowledge not only in its content but also in its cognitive states. To address the complexity of knowledge representation, we first redesign the organizational form of knowledge content, as the traditional triplet format proves inadequate. Consequently, we introduce the concept of knowledge scenario based on scenario theory, intending to enrich knowledge with additional attributes beyond the traditional triplet format. These attributes are represented as scenario attributes that encompass both physical and information spaces, emerging through the process of knowledge generation and application. This step accentuates the computation of knowledge content applicability. Moving forward, we proceed to compute knowledge cognitive states by analyzing the certainty strength of the knowledge itself and the supporting strength of external evidence. Through the integration of these two aspects, we successfully achieve knowledge computation.To verify the effectiveness of our proposed method, we conduct two experiments. Firstly, we apply the method to biomedical and health sciences, specifically focusing on Mild Cognitive Impairment (MCI). We construct a scenario-based knowledge graph and establish rules to explore the multi-angle knowledge association characteristics of fusibility, inheritance, and inference. By retrieving knowledge based on scenario matching, we significantly enhance the applicability of the knowledge. Secondly, we employ ChatGPT, a representative large language model, and integrate our method as a prompting engineering following a specific chain of thought. By applying this approach to the diagnosis of MCI, we effectively mitigate the occurrence of hallucinations in the large language model, substantially improving the reliability of knowledge.Joanne Dehnbostel, MS, MPH, Computable Publishing LLC, Scientific Knowledge Accelerator Foundation[email protected]Brian S. Alper, MD, MSPH, Computable Publishing LLC, Scientific Knowledge Accelerator Foundation[email protected]Khalid Shahin, BA, Computable Publishing LLC, Scientific Knowledge Accelerator Foundation[email protected]Joshua Richardson, PhD, MS, MLIS, FAMIA, RTI International[email protected]Standard terminologies facilitate unambiguous communication in many domains. However, there are no globally accepted and adopted standard terminologies for reporting scientific knowledge. A global effort started in 2020 to define standard terminologies (in the form of code systems) for four scientific knowledge concepts: Study Design, Risk of Bias, Statistic Type, and Statistical Model.The effort created a Code System Development Protocol to support global development of terminologies for exchanging scientific evidence. Initial steps of the protocol included (1) assembling expert working groups with people from more than 25 countries, (2) identifying 23 commonly used tools and systems for which standard terminology would be useful, (3) drafting 368 non-redundant concepts to become display terms for the four code systems, (4) identifying 27 ontologies with related terms and definitions, and (5) mapping available terms and definitions for the draft concepts. (Alper BS, Dehnbostel J, Afzal M, Subbian V, Soares A, Kunnamo I, Shahin K, McClure RC, For the COVID-19 Knowledge Accelerator (COKA) Initiative. Making Science Computable: Developing code systems for statistics, study design, and risk of bias. Journal of Biomedical Informatics 2021 Mar;115:103685. https://doi.org/10.1016/j.jbi.2021.103685).We consolidated these efforts into a single Scientific Evidence Code System (SEVCO), developed tooling to facilitate the work (including FEvIR®: CodeSystem Builder/Viewer and FEvIR®: My Ballot), and streamlined the protocol to enable more efficient development processes (Alper BS, Dehnbostel J, Lehmann H, Whaley P, Wilkins KJ, Tufte J, Yurk RA, Ojha N, Afzal M. For the COVID-19 Knowledge Accelerator (COKA) Initiative. Scientific Evidence Code System Development Protocol. Created November 16, 2021. Last revised December 8, 2021. Available at: https://tinyurl.com/SEVCOprotocol).The SEVCO development is available for open viewing at. https://fevir.net/resources/CodeSystem/27270#TOP and anyone can comment on any term. Participation in terminology deliberations is also open to anyone. You can self-select to join the SEVCO Expert Working Group at https://fevir.net/resources/Project/27845 and as terms are open for voting, you can vote Yes or No and/or provide comments for what changes may be needed. Terms are passed when they receive 100% agreement with at least five votes, and negative votes lead to discussion and modification until they reach approval.As of July 18, 2023, SEVCO has 595 terms, of which 342 (57.5%) have unanimous approval.Once completed, the code system should more effectively facilitate identifying, processing, and reporting research results and the reliability of those results. More efficient and detailed scientific communication will reduce cost and burden and improve health outcomes, quality of life, and patient, caregiver, and healthcare professional satisfaction.SEVCO is developed by the Health Evidence Knowledge Accelerator (HEvKA), which is an open, virtual group to accelerate identifying, processing, and disseminating computable biomedical knowledge, especially related to clinical research. HEvKA efforts inform and develop standards, terminologies, and tools for computable expression of evidence and guidance. You can participate through any of 15 open virtual weekly meetings. Details at https://tinyurl.com/HEvKAAllen Flynn, University of Michigan Medical School and School of Information[email protected]What rights should CBK creators and Metadata Contributors enjoy, and how will those rights be upheld? With written knowledge production, journals identify and authenticate authors, uphold standards via peer review, and maintain rights. Namely, via journals, rights of attribution and integrity of authors' works are upheld, incentivizing publication.To have a trusted and economically viable online CBK ecosystem, identification, authentication, and attribution (IAA) of all creators and contributors is required. Yet the online world that has evolved to date fosters anonymity, not IAA. In his 2016 piece “How to fix the internet”1, Walter Isaacson prescribed changes to counter anonymous malicious online actors. We apply Isaacson's ideas to envision a trusted online CBK ecosystem that incentivizes voluntary, verifiable IAA.Voluntary, verifiable IAA enables creators of CBK artifacts and metadata to benefit when others use what they produce. By upholding creators' rights of attribution and the integrity of CBK works, creators can benefit via recognition and payments. In addition, for economic viability to be achieved, these creators must be shielded from liability that cancels out such benefits. Only when creators can gain from participating in a future CBK ecosystem should we expect routine compliance with voluntary, verifiable IAA to follow.We imagine these five items are necessary, if not sufficient, to establish suitable economic incentives for any viable future CBK ecosystem.1 Isaacson W. How to fix the internet. The Atlantic. 2016 Dec;15.David Hanauer, University of Michigan[email protected]Lisa Ferguson, University of Michigan[email protected]Kellen McClain, University of Michigan[email protected]Guan Wang, University of Michigan[email protected]Approaches for achieving computable biomedical knowledge (CBK) in the clinical domain often require the use of unstructured (free-text) clinical notes from electronic health records (EHRs). There remain significant challenges for broad use of unstructured clinical data, in part because many natural language processing (NLP) and text retrieval/processing systems are too complex to use for non-technical users. Our team has been developing tools for enabling non-technical research and operational teams within healthcare settings to securely access and utilize unstructured EHR notes for a variety of purposes ranging from clinical research to quality improvement in healthcare systems.EMERSE—the electronic medical record search engine—is a text retrieval and text processing system to help “democratize” the use of free text data in EHR notes, with a special emphasis on usability for non-technical users. EMERSE has many features specifically designed to support biomedical research, including robust query expansion that can leverage similar terms from multiple vocabularies and ontologies (e.g., Human Phenotype Ontology, Orphanet Rare Diseases, the Gender, Sex, and Sexual Orientation Ontology, and more). EMERSE securely enables network-based searches to obtain obfuscated patient counts across participating institutions. EMERSE also supports collaboration and re used of search terms through a feature in which collections of terms/phrases can be shared and re-used by other teams. EMERSE also groups notes by patients, making it easier to identify cohorts for tasks such as eligibility determination for clinical trials.New development work underway includes the additional of named entity recognition (NER) and coding notes to the Unified Medical Language System (UMLS) using Concept Unique Identifiers (CUIs). Such an approach will allow users to search for terms and concepts interchangeably. The ability to consider negation status, uncertainty, and whether the text is about the patient or another individual is also planned. EMERSE has been in constant development since 2005 and is now operational at multiple large academic medical centers across the United States and Europe. It is available at no-cost under and open source license.Jianguo Hao, National Institute of Health Data Science, Peking University, Institute of Medical Technology, Health Science Center of Peking University[email protected]Shichao Fang, King's College London[email protected]Qing Li, Advanced Institute of Information Technology, Peking University[email protected]Guilan Kong, National Institute of Health Data Science, Peking University, Institute of Medical Technology, Health Science Center of Peking University, Advanced Institute of Information Technology, Peking University[email protected]Radiological examination is an effective and valuable method for thoroughly inspecting a patient's chest. Clinical data, which can provide valuable supervisory information for machine learning algorithms, is commonly stored in electronic health records (EHRs). Chest radiographs often contain multiple pathologies, making the diagnosis of these multi-label medical images without precise annotations particularly challenging and time-consuming. This study endeavored to combine a hierarchical clustering method called formal concept analysis (FCA) and a contrastive learning paradigm to learn enhanced discriminative representations from EHR data and chest x-ray radiographs (CXRs), with the aim of offering support for downstream tasks, such as similar image retrievals.The data source utilized in this study is the Medical Information Mart for intensive care chest x-ray (MIMIC-CXR) dataset, known as one of the largest de-identified publicly accessible repositories of CXRs for clinical data analysis. Its de-identified structured EHR data contains demographic information, concise clinical interpretations, and meta-data such as orientations of CXRs. It should be noted that each patient may undergo multiple radiological examinations, with each preliminary diagnosis being documented in a free-text radiology report.To encode CXRs, we leveraged a deep learning model, EfficientNet, as the backbone network. The model was initialized with pretrained weights obtained from the ImageNet dataset and then fine-tuned for transfer learning using contrastive learning to adapt the MIMIC-CXR dataset. The model further incorporated a pretext task employing a hierarchical clustering algorithm to cluster CXRs. Categorical EHR data extracted from free-text reports, such as pathologies or CXR findings were served as a source of supervisory information. The MIMIC-CXR dataset was split into training, test, and validation subsets. Subsequently, FCA approach generated a hierarchical clustering graph that clustered the CXRs based on various sets of supervisory information. In the generated graph, clusters were systematically organized and interconnected based on their mutual correlations of supervisory information. It could facilitate a coherent representation of relationships and dependencies among the clustered CXRs, providing valuable insights into their similarities and dissimilarities. Given an anchor CXR, the graph classified indexed CXRs as either positive or negative cases by considering the relationship between their supervisory information. To develop the instance discrimination model using contrastive learning, triples consisting of anchor CXRs together with their corresponding positive and negative CXRs, were formed in the training process. These encoded triples were designed to continuously optimize the model by a contrastive loss to get similar CXRs closer and pull away dissimilar ones. This optimization process made the learned representation space of CXRs more discriminative. Compared to self-supervised contrastive learning, the proposed hierarchical contrastive learning performed better in the downstream task of cross-modal retrieval for similar cases. The average retrieving precision of the top 5 retrieved similar cases increased from 76.80% to 81.01%.To summarize, we proposed a supervised hierarchical contrastive learning approach to learn discriminative representations from multi-label CXRs. By combining contrastive learning with hierarchical clustering, the proposed model reinforces the hierarchical supervision and makes all the studied cases well represented, and thus enables better discrimination of multi-label CXRs.”Xia Jing, Clemson University[email protected]Richard D. Boyce, University of Pittsburgh[email protected]Hua Min, George Mason University[email protected]Yang Gong, University of Texas Health Science Center at Houston[email protected]James J. Cimino, the University of Alabama at Birmingham[email protected]Dean F. Sittig, University of Texas Health Sciences Center at Houston[email protected]Clinical decision support systems (CDSS) have played a critical role in delivering safe, efficient, and quality clinical care. Rule-based CDSS have a long history of use in large medical institutions because they need to maintain ever-evolving rules. However, sharing computable CDSS rules across institutions has yet become commonplace. Our international collaboration group aims to use ontology to develop computable CDSS rules, particularly for resource-limited primary care settings. The work could provide usable, maintainable, up-to-date, and computable CDSS rules for primary care settings that lack in-house IT professional support. We have used CDC-recommended vaccination schedules (≤18 years, 2022 version, five tables with annotations) as the starting CDSS rules.Translating the vaccination recommendations (start point) directly into machine-processable formats (endpoint) is unrealistic. Several mediate layers are needed between the start and endpoints; for example, a detailed tabular format compiled from the current CDC vaccination recommendation schedules is required; then, a detailed and thorough version represented in formal language formats can be easily converted to different programming languages. We aim to use unambiguous formats to represent the human-readable CDSS rules from tabular formats to one that programmers can use to develop machine-processable formats accurately. This strategy also minimizes errors in generating the CDSS rules in different programming languages.We have completed the first mediate layer with over 465 rules for 19 vaccines in tabular format. We are currently generating the second mediate layer: a detailed and accurate version of the CDSS rules in formal languages, such as Web ontology language (OWL), clinical quality language (CQL), or Drools. The formal language formats can be translated into programming languages based on the deployment system requirements. Meanwhile, we are constructing a CDSS ontology to organize the mapping between concepts and individual terms across terminologies, both of which are used in representing the CDSS rules in mediate and final versions. OWL can provide reasoning potential; however, there are limitations to representing the complex and exact logical expressions typical of CDSS rules. CQL seems more promising in representing logic expressions. However, downstream use needs further demonstrations. Drools has been used in generating business logic and rules; however, like OWL and CQL, there is a deep learning curve to overcome in using Drools and setting up the work environment.Although there will be challenges along the way, this is a feasible pathway for generating computable CDSS rules, a critical milestone in achieving true interoperability. Converting the 5-table CDC vaccination recommendations into detailed tabular formats and then to CDSS rules in formal languages bridges human comprehensible representations and machine-processable formats with explicit, clear, accurate representations of CDSS rules. It is a step toward achieving truly interoperable patient records, a long-time dream yet to be realized in healthcare IT.Inwon Kang, Rensselaer Polytechnic Institute[email protected]William van Woensel, University of Ottawa[email protected]Oshani Seneviratne, Rensselaer Polytechnic Institute[email protected]We explore using Large Language Models (LLMs) to generate computable knowledge in the form of decision logic (Notation3, Clinical Quality Language) and smart contracts (Solidity) that encode health insurance processes found in natural text. We present an initial methodology that generates output at increasing levels of complexity and technical detail: i.e., with generation tasks (1) structured summaries, (2) formalized decision logic, and (3) smart contract code. The LLM's output at each level can support a domain expert or Web3 developer in authoring decision logic or smart contracts. We engineered our LLM prompts following best practice guides provided by OpenAI and Deep learning AI. We propose experiment metrics, including completeness, soundness, clarity, and syntax, to evaluate the LLM's output. Our evaluation employs three textual scenarios on health insurance processes, progressively increasing in difficulty in length and comprehensibility for non-domain experts, sourced from Medicare's official booklet. Our findings indicate that the LLM performs well in generating structured textual summaries across all scenarios. However, for tasks (2) and (3) that generate decision logic and runnable smart contracts, implementing particular health insurance processes, we found that the model's output requires human oversight to ensure validity. Notably, the model does not apply basic conceptual modeling principles, meaning that when runnable, the generated code will not be sound (i.e., yielding false positives or negatives). Moreover, the most advanced scenario seems too complex for the LLM to generate a correct set of decision logic and code, with more complex conditions being outputted as natural language comments instead of code, and other conditions simply missing. However, one of our decision logic targets, Clinical Quality Language, has very poor syntax for all scenarios, likely due to the lack of online training data. Nevertheless, our experiments demonstrate the promise of LLMs in supporting the translation of process descriptions from dense natural text into formal decision logic and smart contracts, which may aid streamlining many complex processes.Rami Khoury, MD, American College of Emergency Physicians Board Member, Independent Emergency Physicians-PC[email protected]Pawan Goyal, MD, MHA, American College of Emergency Physicians[email protected]Dhruv Sharma, BA, MS, American College of Emergency Physicians[email protected]Data is driving the future of medicine. We've already seen the critical importance of real-time insights to new and emerging health threats during the COVID-19 pandemic, as well as the impact of health care trends and patterns of resource utilization. With the new Emergency Medicine Data Institute (EMDI), the American College of Emergency Physicians (ACEP) is rapidly moving emergency medicine to the forefront of data-driven quality and practice innovation. This new initiative is poised to become a source of truth for all emergency medicine data. Harnessing the power of information that physicians are already recording, ACEP collates vital metrics from emergency departments nationwide to support research and grants, while enhancing value for emergency physicians, patients, and the broader health care community. The presenters will enumerate the current, state-of-the-art, and future challenges facing emergency medicine that will be addressed by the EMDI, illuminated with examples of data use from other medical specialties and early success stories. Attendees will be given a sense of emerging trends in clinical, quality, and economic aspects of emergency care that demonstrate the value and power the EMDI brings to the specialty.Guilan Kong, National Institute of Health Data Science at Peking University, Advanced Institute of Information Technology, Peking Universit.[email protected]Jiayu Wang, Institute of Medical Technology, Peking University Health Science Center[email protected]Bixia Gao, Renal Division, Department of Medicine, Peking University First Hospital[email protected]Jinwei Wang, Renal Division, Department of Medicine, Peking University First Hospital[email protected]Luxia Zhang, National Institute of Health Data Science, Peking University[email protected]Minchao Liu, Department of Computer Application and Management, Chinese PLA General Hospital[email protected]Diabetes is a serious public health problem around the world. Nowadays, the prevalence of diabetes in low- and middle- income countries (LMIC) has been rising, and the increase of disease burden of diabetes in LMIC is faster than high income countries. Moreover, various types of complications may come with diabetes, and thus a heavy economic burden has been put on both patient families and the society. Conventionally, diabetes was classified into two types—Type 1 and Type 2—which are also known as “Insulin Dependent” and “Noninsulin Dependent” diabetes, respectively. The treatment methods for Type 1 and Type 2 diabetes are different accordingly. In practice, due to the high degree of heterogeneity among diabetes, the clinical characteristics and outcomes of Type 2 diabetes patients are very different as well.With the advances of big data, the concept of computable phenotypes was proposed to classify patients into different subtypes based on clinical data characteristics. The patients belonging to the same disease subtype may have similar complication risk, and the treatment strategies of patients with common clinical characteristics may be similar.In diabetes, a data-driven subgroup analysis was conducted on a Swiss diabetes cohort in 2018, and five subtypes were identified for diabetes patients based on six clinical variables, including glutamate decarboxylase antibodies, age at diagnosis, body mass index (BMI), HbA1c, and homoeostatic model assessment 2 estimates of β-cell function (HOMA2-B) and insulin resistance (HOMA2-IR). Each subtype has different patterns in complication occurrence. Several studies have been conducted among different ethnicities around the world to validate the existence of the identified 5 subtypes of diabetes.In this study, the clinical data of diabetes patients going to the clinic of or being admitted to a Grade A Class 3 hospital in Beijing from 2000 to 2020 was used as data source. As glutamate decarboxylase antibodies is a clinical variable related to Type 1 diabetes and rarely recorded in the electronic medical records (EMR) system, diabetes patients with the data of five clinical variables (age, BMI, HbA1c, HOMA2-B and HOMA2-IR) recorded around the time of diabetes diagnosis were included for analysis. According to the central point value of each clinical variable in the five subtypes identified by the Swiss study, the distance between the clinical vector (age, BMI, HbA1c, HOMA2-B and HOMA2-IR) of each patient and the central point vector of each subtype was computed, and then each patient was classified into the subtype with the shortest distance. Therefore, each patient included for analysis was assigned a subtype based on his or her clinical characteristics at diabetes diagnosis. Finally, the patterns of diabetic complication occurrence were analyzed for the studied diabetes patients, and similar disease progression patterns have been found for patients labeled with the same subtype.The computable phenotyping tool developed in this study had the capability to aid physicians to grouping Type 2 diabetes patients, and thus has the potential to facilitate precision and personalized medicine. Ideally, a recommendation of clinical treatment methods can be provided for each diabetes subtype after further analysis.Zach Landis-Lewis, University of Michigan[email protected]Allen Flynn, University of Michigan[email protected]Peter Boisvert, University of Michigan[email protected]Hana Chung, University of Michigan[email protected]Patrick Galante, University of Michigan[email protected]Ayshwarya Jagadeesan, University of Michigan[email protected]Mobilizing CBK requires that its implementers and key stakeholders efficiently develop an understanding of its purpose and function. However, supporting learning about CBK can be challenging due to the complex and abstract nature of algorithms, knowledge graphs, and computational models. Furthermore, when diverse models are implemented in software applications, the effort required to learn about a model collection can become prohibitively high. Methods to better support learning about CBK for its successful mobilization are needed.Vignettes are tutorial-like technical documentation, written for learning about R statistical software packages. Vignettes make complex software easier to understand by providing examples of data and analyses with supporting discussion and interpretation. To our knowledge, a practice like that of developing vignettes for CBK has not been widely adopted, yet writing vignettes may benefit CBK developers, implementers, and stakeholders in promoting more efficient learning about models and model collections.We have developed 10 vignettes for a model collection in a precision feedback system. The system, which has been implemented for anesthesia providers, prioritizes motivational feedback messages about care quality based on the preferences of feedback recipients. The model collection includes logic models, ontologies, and algorithms which interact through modules of a software pipeline, implemented in a web service to support clinical quality dashboard and email content.Each vignette is implemented as a readme file in a public GitHub repository. We developed each vignette with two characters (i.e., personas) whose differing preferences result in the selection of different messages by the precision feedback system. The vignettes describe system components and models, and include links to external resources, such as ontology terms and classes in BioPortal.Through the vignette development process, we have begun to recognize multiple types of value. Our driving purpose has been to provide an accessible resource for learning about the precision feedback system by system implementers and stakeholders. However, the process of writing the vignettes has resulted in enhanced team communication and improved organization of system development tasks.Furthermore, we have begun using the vignettes to guide system testing activities, including optimizing system performance. As a result of these insights, we plan to develop the vignettes as documentation that is packaged with the model collection and for future software releases of the precision feedback system.Wentie Liu, National Institute of Health Data Science, Peking University[email protected]Tongyue Shi, National Institute of Health Data Science, Peking University[email protected]Shuai Jin, Department of Adult Care, School of Nursing, Capital Medical University[email protected]Jianguo Hao, National Institute of Health Data Science, Peking University, Institute of Medical Technology, Health Science Center of Peking University[email protected]Huiying Zhao, Peking University People's Hospital[email protected]Guilan Kong, National Institute of Health Data Science at Peking University, Advanced Institute of Information Technology, Peking University[email protected]Sepsis is life-threatening organ dysfunction caused by a dysregulated host response to infection. It leads to high in-hospital mortality, particularly for intensive care unit (ICU) patients. Identifying septic patients who are at high risk of in-hospital mortality early on can aid ICU physicians in making optimal clinical decisions. This study aimed to develop machine learning-based tools that can predict hospital mortality risk for septic patients in the ICU based on clinical data gathered in the first 3 hr of sepsis.Fluid therapy represents a clinical treatment approach which focuses on maintaining fluid homeostasis through the supplementation or restriction of specific fluids. As a life-threatening condition, early resuscitation volume is important for sepsis patients, and it impacts patient prognosis and outcomes. Regrettably, most existing predictive models for sepsis mortality have not incorporated the early resuscitation volume for analysis. In clinical practice, early fluid resuscitation is advocated for sepsis patients. The 2016 Surviving Sepsis Campaign guideline recommends that at least 30 mL/kg of intravenous (IV) crystalloid fluid should be given within the first 3 h of resuscitation to patients with sepsis. As can be seen from the guideline, the initial 3 h following the diagnosis of sepsis are considered as the critical “golden time” for early resuscitation. Therefore, in this study, the intervention of early resuscitation was incorporated in mortality risk prediction modeling. A comprehensive set of clinical variables, which were collected in the first 3 h of sepsis diagnosis, together with the volume of crystalloid fluid administered during this initial 3 h were included for analysis.The Medical Information Mart for Intensive Care-IV(MIMIC-IV) database containing records of over 40 000 ICU patients admitted to the ICUs at Beth Israel Deaconess Medical Center between 2008 and 2019, was used as data source. The sepsis patient data extracted from MIMIC-IV formed a large study population and the clinical information contained including demographics, lab tests, clinical assessments and medical treatments. Around 80% of sepsis data was used for model development and the remaining for model test.In terms of analytics, several machine learning methods with good explainability, which have also demonstrated satisfied predictive capabilities in medicine, including Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), together with multivariate logistic regression were used in this study. Finally, prediction performance of two machine learning-based models was compared with that of the traditional logistic regression, and the prediction model with best performance was selected for clinical recommendation.The predictive tool developed in this study will help to identify sepsis patients with high in-hospital mortality risk at an early stage. Hopefully, it will aid ICU physicians to provide timely and optimal interventions, and thus may help to improve ICU patient outcomes and reduce in-hospital mortality.Joshua Martinez, Stanford University[email protected]Cataracts are the leading cause of vision impairment globally, and cataract surgery is the most commonly performed surgery in the United States. Continuously enhancing surgical training is key for ensuring high surgical standards and maintaining excellent visual outcomes of surgery. Traditionally, surgical training and feedback is based almost solely on preceptor-led real-time feedback in the operating room. The advent of machine learning, specifically deep learning models, has the potential to augment surgical feedback by enabling more granular and highly objective analysis of surgical performance through analysis of routinely captured surgical video. In previous work, we developed deep learning models that could identify key surgical landmarks and the surgical steps being performed in cataract surgery videos, thereby offering a novel means of quantitatively assessing surgical skill. We present here an implementation of these models into an application called PhacoTrainer, which is a platform designed to provide cataract surgeons with objective, automated feedback on their surgical techniques, and to facilitate future research on cataract surgery.The PhacoTrainer platform is a web-based application to which users can upload cataract surgical video and receive insights into their cataract surgical performance. The platform deploys a deep learning model, a hybrid Convolutional Neural Network and Recurrent Neural Network, to uploaded videos to detect which surgeries include special surgical techniques or complications. The model outputs also enable calculation of the time spent on each step of surgery, which then is displayed in a dashboard visualizing the change in surgical times for each step as a surgeon accumulates more experience. The timeline of each surgical video is also automatically annotated with a frame-by-frame determination of which surgical step is being performed, to better allow surgeons to browse through surgical video. Thus, the feedback provided by PhacoTrainer based on these models equips surgeons with insightful metrics to monitor their surgical performance across multiple dimensions, identifying areas for potential improvement.The PhacoTrainer platform heralds a significant advancement in cataract surgery training and transforms unstructured cataract surgical video into computable insights. By mobilizing deep learning to objectively analyze surgical videos, it provides surgeons with a tool to self-evaluate their skill, track improvements, record surgical metadata, and ultimately enhance surgical outcomes. PhacoTrainer also makes high quality feedback available to all trainees, irrespective of geographical or institutional constraints. With its capability to accumulate a vast repository of metadata on cataract surgery, PhacoTrainer also promises to catalyze future research on cataract surgery, facilitating a more nuanced understanding of surgical techniques over time and cataract surgical training.”Alan H. Morris, MD, University of Utah[email protected]For the replicable decision-support group.Clinical decision-making is based on knowledge, expertise, and authority, with clinicians approving almost every intervention according to the Hippocratic model of clinical decision-making. This is the starting point for delivery of “All the right care, but only the right care,” a quality goal yet unachieved because unaided clinicians suffer from human cognitive limitations and biases when decisions are based only on their training, expertise, and experience. Robust decision-support tools that reduce unwarranted variation of clinician decisions and actions can improve healthcare. Current electronic health records (EHRs) are focused on results review, documentation, and accounting. EHRs are awkward, time consuming, and contribute to clinician stress and burnout. Decision-support tools can reduce clinician burden and enable replicable clinician decisions and actions that personalize patient care. However, most current clinical decision support tools/aids lack detail and neither reduce clinician burden nor enable replicable clinician actions. Clinicians must provide subjective interpretation and missing logic, thus introducing personal biases and mindless, unwarranted, variation from evidence-based practice. Replicability occurs when different clinicians, with the same patient information and context, come to the same decision and action. A subset of therapeutic decision support tools based on credible clinical outcome evidence exists as computer protocols (eActions), including closed-loop systems, that lead to replicable clinician actions. eActions enable different clinicians to make consistent decisions and actions when faced with the same patient input data and context. In advanced modern healthcare delivery environments, eActions have overcome cognitive limitations of overburdened clinicians. eActions embrace good everyday decision-making informed by evidence, experience, EHR data, and individual patient status. eActions can reduce unwarranted clinician variation, increase quality of clinical care and research, reduce EHR noise, and could enable a learning healthcare system.Delivering best evidence-care remains a vexing problem that is achieved only ~50% of the time in advanced healthcare systems. Evidence-based guidelines can address only a small fraction of the types of clinical care. Underserved areas rarely can access state-of-the-art evidence-based guidelines in real time, and often cannot implement advanced guidelines. Care providers in such settings frequently do not have sufficient training or time to implement advanced guidelines. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems that could yield new or modified evidence-based guidelines derived from comparative effectiveness clinical research carried out during routine healthcare delivery within true learning healthcare systems. Developing countries could likely benefit as well from focused applications using limited healthcare electronic systems and smart phones.Katelin Morrissette, University of Vermont Medical Center[email protected]Many important components of medical decision making, such as diagnostic certainty, interventions considered but avoided, or patient input in the management decisions are challenging to measure from existing data elements in the medical record. We present a method to build custom data elements to reflect these components of medical management and describe the implementation process. New innovations in medical management may not be represented in traditional elements of the electronic health record and will rely on these customized data elements as well. For example, in critical care medicine the phase of care before a patient is admitted to an intensive care unit (ICU) can be thought of as peri-ICU. This peri-ICU phase is when interventions which may avert an ICU admission, or define initial diagnosis and management occur. We have used features of the Epic EHR to create computable knowledge customized to this specific phase of care and a critical care consult service. The consult service bridges hospital departments, utilizes tele-health, and seeks to treat critical illness outside the walls of an ICU. It was important to understand the impact on resource utilization as well as medical decision making and required customized data to be easily reported, shared, and improved over time. We use this case example to demonstrate feasibility and the implementation process for how computable data elements can be customized for specific clinical scenarios, rapidly deployed in line with clinical needs, and has potential to stimulate data sharing for concepts that were previously ambiguous and difficult to quantify.Jerome A. Osheroff, TMIT Consulting, LLC and University of Utah Department of Biomedical Informatics and Department of Veterans Affairs[email protected]Brian S. Alper, Computable Publishing LLC[email protected]Philip Barrison, University of Michigan Medical School Department of Learning Health Sciences[email protected]Mary Butler, University of Minnesota[email protected]Joshua E. Richardson, RTI International[email protected]Ken Rubin, University of Utah Department of Biomedical Informatics; Department of Veterans Affairs[email protected]Janice Tufte, Hasannah Consulting[email protected]for the Pain/Opioid LHS Learning Community https://docs.google.com/document/d/1J_Td8uQsXi9p0HWzDGsRmI7Dg3x-FBo3BWtzN5k-Pe8/edit?pli=1Healthcare is far from realizing the Quintuple Aim of optimizing health at reduced costs while increasing equity and patient and clinician satisfaction. Aspiring Learning Health Systems (LHSs) are attempting to close this gap by enhancing the cycle whereby healthcare evidence is used to guide care—and care delivery results are used to continually improve available evidence. Interoperable, computable biomedical knowledge (CBK) is essential to optimize this cycle.From 2018 to 2021, the AHRQ Evidence-based care Transformation Support initiative (ACTS) engaged over 330 individuals from diverse stakeholder groups to produce a stakeholder-driven future vision and action plan—and conduct pilots and concept demonstrations—for a knowledge ecosystem that supports CBK-enabled LHS cycles. After ACTS, the project lead (Osheroff) formed the LHS Collaborative to drive stakeholder-supported efforts to execute the ACTS action plan. The LHS Collaborative brings together diverse LHS stakeholders to enhance care transformation toward the Quintuple Aim and LHS function for clinical targets where improvements are urgently needed.The Pain Management/Opioid Use LHS Learning Community (POLLC) is the first and most advanced LHS Collaborative community formed in follow-up to the ACTS initiative. POLLC includes multiple stakeholder groups (e.g., care team members, guideline developers, health IT suppliers, quality improvement organizations, standards experts, and patient advisors) who are working together to identify and close pain and opioid management care gaps in care delivery organizations (CDOs). Other targets being addressed by public and private CDOs supported by LHS Collaborative tools and approaches include hypertension control, sickle cell disease management, venous thromboembolism (VTE) prophylaxis, and chronic kidney disease.This Lightning Talk will illustrate how CBK-related activities around the LHS cycle by the POLLC collaborative community and others could be integrated and enhanced to accelerate care transformation and LHS cycle function for this target. It will also outline how this approach could be scaled to address other high-priority conditions by ensuring that various target-focused LHS efforts contribute synergistically to building out a robust national/global knowledge ecosystem. In particular, by fostering a public-private partnership to drive progress toward the shared future vision, as called for in the ACTS action plan.The authors plan to leverage this lightning talk and related explorations to transition the potential opportunities to leverage CBK to improve LHS cycle efficiency and effectiveness into real world advancements. And do this in a manner that cultivates synergies with other CBK-related activities to accelerate progress toward shared LHS goals.”Philip Scott, University of Wales Trinity Saint David[email protected]Charlie McCay, Ramsey Systems[email protected]Shaun Rowark, NICE[email protected]Background: The National Institute for Health and Care Excellent (NICE), a non-departmental public body under the Department of Health and Social Care in England, has made a strategic commitment to producing clinical practice recommendations in a more modular and digital approach. In 2022–23, a first phase of work explored methods to convert narrative clinical recommendations to computable knowledge. The project involved NICE, MCBK-UK and a broad stakeholder group from industry, academia and healthcare providers. Two in-person ‘collaborathons’ were held, with fortnightly workstream calls in between, focused on NICE Guideline 28 (NG28), Type 2 diabetes in adults, specifically section 1.7 on drug treatment. The project adopted the World Health Organization (WHO) Digital Adaptation Kit (DAK) as a technology-agnostic method to model clinical practice recommendations. A NICE Computable Implementation Guide (NCIG) following the structure of the DAK was produced for NG28 section 1.7, including user scenarios, personae, processes and workflow, core data elements and decision-support logic. The first phase of work concluded in March 2023, with a recognition that further work was needed to demonstrate real-world utility, define indicators (outcomes such as prescribing compliance) and functional/non-functional requirements (such as data quality and usability) and to refine the documentation structure. Methods: The defined scope of the second phase started with real-world implementation. The project was loosely coupled with concurrent work on the diabetes information standard commissioned by NHS England from the Professional Record Standards Body (PRSB), an umbrella body formed from the medical Royal Colleges and professional bodies from across health and care. This enabled multi-disciplinary participation and drew upon PRSB's prior experience in development and implementation of clinical information standards. The selected implementation method was to define document templates (electronic forms) for primary care consultations by general practitioners (GPs) based on the core data elements and decision logic defined in the NCIG. GP document templates are a well-established and relatively straightforward way of standardizing records of care, as they enable pre-population from existing records, prompt for new data collection and can trigger links to practice workflows. Templates will also be defined for use in secondary care, and the effective sharing of information between primary and secondary care in support of the processes selected from NG28 and documented in the NCIG. Results: At the time of writing, GP template production is underway. By the time of the conference, we will report implementation experience of using the template with practices as well as outlining plans for outcome analysis and functional/non-functional requirements. We will also be able to report on initial work in how this is supporting NICE's wider portfolio of guidance products.Simmi Sen, Stanford University[email protected]Eric Yeh, Stanford University[email protected]Sophia Wang, Stanford University[email protected]Background: Cataracts are caused by clouding of the natural lens that occurs with aging, leading to vision loss. Skilled completion of cataract surgery can result in almost immediate restoration of vision, but lengthy surgical time or poor instrument control may lead to prolonged or permanent vision loss. Cataract surgical trainees operate under the supervision of a preceptor who provides real-time feedback which is typically of a qualitative nature. Until now, there has been no way to track important characteristics of surgical performance related to tool usage or eye positioning. We previously developed deep learning methods to recognize the locations of key anatomical landmarks such as the pupil and its center, as well as surgical instruments and their tips from cataract surgical video, allowing for the calculation of AI-derived metrics related to performance, such as the total path length and area covered of individual tools, the velocity of tools, the centration of the eye, among others. The purpose of this study was to investigate whether these new AI-computed cataract surgery performance metrics correlate with surgeon seniority and skill as rated by human experts.Methods: 28 resident and 29 attending routine cataract surgical videos were anonymously collected. For each video, 6 machine-generated metrics were generated by deep learning models: total path length, max velocity, area, phacoemulsification centration, eye fixation, and zoom level change. The former 3 metrics were individually calculated for limbus, pupil, and surgical instruments and the others were obtained at video level. Human raters independently rated the videos by Objective Structured Assessment of Cataract Surgical Skill (OSACSS), which had 20 subitems on a 5-point scale with larger indicating better performance. Statistical differences of machine- and human-rated scores between attending surgeons and trainees were tested by t tests, and the correlations between the two were examined by Pearson correlation coefficients.Results: Phacoemulsification probe and irrigation/aspiration probe had significantly lower total path lengths, max velocities, and area metrics in attending videos. Attending surgeons exhibited better phacoemulsification centration and eye fixation. Most metrics correlated with average human-rated OSACSS scores, including tool-specific metrics and metrics related to microscope control (fixation: −.0.349; zoom level change: −0.322). Machine-generated metrics with corresponding OSACSS subitems also exhibited significant negative correlations (fixation: −0.65, phacoemulsification probe area metric: −0.67).Conclusion: Automatically generated AI-metrics can be used to differentiate between attending and trainee surgeries and correlate with the human evaluation on surgical performance. These metrics can be automatically generated in a fast and scalable way in the post-surgical analysis, enabling surgical trainees to receive useful feedback in a timely manner during their training. In addition, the numerical values of these metrics can be logged and reviewed later to track the improvement in different facets of surgical skills. The model shows promise in building a fully automatic, objective surgical feedback system in ophthalmology training which will allow for the standardized and consistent analysis of surgical techniques.Tongyue Shi, National Institute of Health Data Science, Peking University[email protected]Wentie Liu, National Institute of Health Data Science, Peking University[email protected]Shuai Jin, Department of Adult Care, School of Nursing, Capital Medical University[email protected]Jianguo Hao, National Institute of Health Data Science, Peking University, Institute of Medical Technology, Health Science Center of Peking University[email protected]Huiying Zhao, Peking University People's Hospital[email protected]Guilan Kong, National Institute of Health Data Science at Peking University, Advanced Institute of Information Technology, Peking University[email protected]Data-driven predictive analysis can help physicians in intensive care unit (ICU) to identify patients with high-risk mortality at an early stage, facilitate the provision of personalized interventions, and thus may lead to improved patient outcomes. Most existing studies employed traditional logistic regression or machine learning methods to develop ICU mortality prediction models, and the extreme lab test results during the first 8 h or 24 h of ICU admission were used as data inputs. In fact, there is a high degree of heterogeneity among ICU patients, and the trends of vital signs may be different among patients with different mortality risk. Is it possible to classify patients into different subgroups and then evaluate the ICU mortality risk accordingly?Patient subgroup analysis aims to put patients with similar characteristics into one subgroup. It helps physicians gain a better understanding of disease patterns, facilitates the provision of personalized treatment plans, and eventually, it may help optimize critical care and improve patient outcomes.The Medical Information Mart for Intensive Care (MIMIC-IV) database was used as data source. Time series data of five vital signs, including temperature, heart rate, mean arterial pressure (MAP), respiratory rate, and blood oxygen saturation (SpO2) were extracted for analysis.An ensemble clustering model was developed for patient subgroup identification based on the time series vital signs data. It mainly comprised multivariate time-series feature extraction and clustering methods. Firstly, Time2Feat was employed as the method for time-series feature extraction. Then, different clustering algorithms including K-Means, K-Shape, and Agglomerative Hierarchical Clustering (AHC) were used as candidate clustering methods for subgroup analysis. In clustering, different distance metrics, including Euclidean distance, Dynamic Time Warping (DTW) and Soft-DTW distance were tried. Finally, based on the Davies-Bouldin Index (DBI) and Calinski-Harabaz Index (CHI), a most suitable clustering model was selected, and the optimal number of clusters (k) was determined using the Elbow Method.Furthermore, based on the identified subgroups, an ICU mortality prediction model was to be developed. Patient personal information including age, gender, and medical history, together with the patient subgroup were used for model development. Ideally, if the patient grouping and mortality prediction models can be deployed in practice, when a patient has been in ICU for 8 hr, the patient would be assigned to a specific subgroup and the corresponding ICU mortality risk would be generated in time.To conclude, an ensemble patient grouping model based on multivariate time-series vital signs data in the first 8 hr of ICU was developed in this study, and three distinct subgroups were identified. The development of an ICU mortality risk prediction model is still ongoing. Next step, we plan to implement the patient grouping and mortality prediction models in practice and evaluate real clinical effects.Simon Šuster, University of Melbourne[email protected]Timothy Baldwin, Mohamed bin Zayed University of Artificial Intelligence[email protected]Karin Verspoor, RMIT University[email protected]Evidence-based medicine relies on expert analysis of scientific findings to address various clinical questions. In addition to locating, organizing, and representing this knowledge/evidence in structured form, a critical aspect is determining its quality. This includes identifying potential biases in methodologies, reporting, and publication of individual studies, as well as assessing the strength of larger bodies of evidence. These steps are crucial for synthesizing reliable evidence. However, their complexity and time-consuming nature hinder the accessibility of current medical knowledge.In this talk, we will demonstrate how online collections of systematic reviews and published primary studies can be converted into machine readable datasets that capture key attributes of the evidence. These can then also be used for training and evaluating models for automated evidence assessment.Relying on machine learning and natural language processing, these models can facilitate critical appraisal of medical evidence by not only predicting overall quality but also providing finer-grained justification for different quality criteria.Furthermore, we will touch upon the topics of reliability through calibration of classifier confidence, selective classification (i.e., decreasing classifier's error rate by sacrificing coverage), and algorithmic fairness (i.e., disparity in different performance measures) in the developed models. Addressing these concerns is essential to ensure that the created quality classifiers are both accurate and unbiased. We believe that in practice, such systems are most likely to work well by working alongside rather than replacing medical experts, who usually construct systematic reviews manually. In effect, an automated system can be seen as an additional assessor against which the manual assessment can be compared and potentially revised. As automated approaches to quality assessment become increasingly accurate and reliable in the future, fewer human examiners may be needed, potentially leading to time and resource savings.By integrating machine learning and natural language processing, we work toward the MCKB vision of structuring knowledge and ensuring that it reflects the best and most current evidence and science. This in turn can empower healthcare professionals to make more informed decisions for their patients.Guy Tsafnat, PhD FAIDH, Founder and Chief Scientific Officer, Evidentli Pty Ltd[email protected]Kenny Hong, PhD, Head of Cloud Engineering. Evidentli Pty Ltd[email protected]Evidence Hub is a public, free website specifically designed to host open-source Computable Biomedical Knowledge Objects (CBKO). The site provides specific features to allow users define types of objects, provide applications to handle different objects, and form communities of interest around the objects.Communities are provided with objected-centered open, moderated discussions with the aim of reaching consensus about implementation, correct use, and logic of the CBKO.Evidence Hub has three public interfaces: a moderated discussion forum, a source code repository, and an application programming interface (API). All three interfaces are free for anyone to use. Uploading and editing source code, contributing to discussions, and using the API require free registration. Source code published on the site through the API must comply with a JSON format derived from the Knowledge Grid format: computable source code, a human-readable description, and a technical description of the execution environment for the object. In addition to these elements, the Evidence Hub format also has a meta-data section that includes identifiers, contributors, and version information that Evidence Hub needs to effectively manage permissions and versions of the object.The source code repository has a dedicated “technical” page for each CBKO with code browsing, version history, a download source-code button, an option to create a “branch” copy of the object, and links to other technical resources such as technical documentation. Contributions to the source code can be uploaded, but inclusion in the object is subject to approval by the object's owner.Each CBKO also has a “non-technical” page. The main features of this page are a human-readable description of the object and a discussion forum. The human readable description of the object is included in the object structure and is displayed here. The discussion forum is multi-threaded and moderated by the object's owner.Both pages have several common elements. Users that were logged in through a registered application (see API description below), will also see a one-click import button on pages of CBKOs corresponding to the application. Social features such as sharing, posting, and watching CBKO's are also found on both pages.The API provides similar functionality to the technical interface as well as endpoints to define new object types, and to register an application's capability to edit and/or execute an object of a particular type. The option to use the application will only be available to users already logged in through the application.Knowledge hosted on the Evidence Hub remains with its owner. CBKOs must have a GPL 2.0 or compatible license. Evidence Hub is free of advertising and user information is only ever used to improve the Evidence Hub itself.Minerva Viguera Moreno, Universidad Nacional de Educacion a Distancia (U.N.E.D)[email protected]María Eugenia Marzo-Sola, Hospital San Pedro, Spain[email protected]Fernando Martin-Sanchez, Hospital Universitario La Paz[email protected]Multiple Sclerosis is a neurodegenerative disease which shows different phenotypes making difficult for clinicians to make short-term decisions related with treatment and prognosis. Diagnosis is usually retrospective. Learning Healthcare Systems (LHS) can support clinical practice as they are devised as constantly improving modules. LHS can identify insights which allow evidence-based clinical decisions and more accurate prognosis.We are developing a LHS with the aim of reducing uncertainty. We are using REDCap™ to collect patients' data, both from Clinical Reported Outcomes (CRO) and from Patients Reported Outcomes (PRO). Once analyzed, this data will serve as a foundation to our LHS. We conducted bibliographical research to select those CRO and PRO collected in clinical practice or identified as possible risk factors. We designed a data collection and management protocol based on using REDCap™ which ensures anonymity and allows longitudinal and semi-automated capture of health data. We are following a cohort of 300 patients for 18 months. At the moment, we have included 198 patients and received 141 complete responses and 7 partial responses regarding PROs baseline (70%). We are collecting PROs every 3 months; until the date, 59 patients have reach end this first endpoint: 32 answered and 27 have been requested by mail (54%). This data will be used to develop a LHS, able to accurate prognosis as well as to automatically include new data and improve its algorithm.Although our results are preliminary, we are finding promising response rates from patients in the first round. On the other hand, rates from the second round are low and could compromise the longitudinal information needed for the LHS development.Hannah Wood, NHS England[email protected]Large Language Models (LLMs) excel at language-based tasks and can save considerable time if used appropriately and with skill. In this session, I will demonstrate that with careful prompting, LLMs like Bard and GPT-4 can generate robust and comprehensive Boolean search strategies for use in Google and advanced search databases.With urgent time pressures in healthcare becoming more prominent than ever before, and an ever-increasing body of healthcare knowledge, LLMs provide an opportunity to save time without necessarily compromising quality. With newly accessible technologies, new search skills are required to responsibly search for healthcare topics.While LLM responses are generally regarded as untrustworthy due to concerns around hallucination risks, bias and misinformation, language models are ideally placed to generate efficient and extensive Boolean search strategies for use in search databases. However, to achieve robust search strategies, knowledge of prompting tools for appropriate responses is required.There are certain elements to avoid or be cognisant of when prompting LLMs to generate or develop search strategies: avoiding the generation of inaccurate Medical Subject Headings and search operators incompatible with search databases and ensuring that the search strategy itself is factually accurate.Language models excel at tasks involving language; they can generate various synonyms and expand upon Boolean search blocks when prompted. These search blocks can be effectively used in Google and other search databases, improving search results, and locating relevant literature. They can also be useful for developing existing strategies.With over 6 months of extensive testing, I conclude that language models have a place in the growing toolset of healthcare Knowledge and Library specialists, and those who frequently search the healthcare literature. LLMs can generate quality search blocks when prompted by a skilled searcher with an understanding of healthcare topics and experience with using Boolean logic in search databases; ultimately saving time and improving the quality of the searches.Jeremy C Wyatt, University of Southampton[email protected]Philip Scott, Programme Director, Institute of Management and Health, University of Wales Trinity Saint David[email protected]Matthew South, Director and Chief Technology Officer, OpenClinical CIC and Senior Software Architect at University of Birmingham[email protected]Mark Thomas, Associate Professor of Cardiology, University of Birmingham[email protected]Caroline Jones, Associate Professor, Hillary Rodham Clinton School of Law, Swansea University[email protected]Our aim in this project (sponsored by the UK MCBK chapter and the British Computer Society) was to understand which knowledge objects in a computable biomedical knowledge library are likely to be subject to regulation as a medical device in the UK. To achieve this understanding, a briefing paper was circulated to a multi-disciplinary group of 25 people including regulators, lawyers, software engineers, digital health academics, librarians, and others with insights into knowledge management and device regulation. A one-day workshop was then convened to discuss some key questions relating to our aim. Following wide ranging discussion by participants and further assessment of relevant regulations, a discussion paper was drafted by lead authors and circulated to other authors for their comments.This contribution will report on how UK medical device regulators are likely to treat the different kinds of knowledge objects that may be stored in computable biomedical knowledge libraries. While our focus is on the likely approach taken by UK regulators, our UK regulators have contributed to the work of the International Medical Device Regulators Forum so our analysis will also be relevant to the approaches taken by regulators elsewhere.We outline the UK criteria for medical devices and the responsibility of device manufacturers, then examine the regulatory implications for knowledge objects that correspond with each of the four knowledge levels described by Boxwala in 2011. We also propose an additional knowledge level for tagged fragments of guidelines etc. that we call level 2b, which is positioned between level 2 and level 3.Our main conclusion is that if a knowledge object is directly executable and is described or marketed as being intended for a medical purpose to provide decision support, it will be in scope of UK regulation as “software as a medical device”. Conversely, if the knowledge object is not directly executable (e.g., it consists of an algorithm, a ruleset, an order set, pseudocode or some other knowledge representation) or the developers make no claim that the object can be used directly for a medical purpose (e.g., it is presented as pluripotential, so could be used to support medical research or education), it is not likely to be subject to UK regulation.We expect similar reasoning to be applied in other countries with similar regulatory principles.","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 3","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10443","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Learning Health Systems","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/lrh2.10443","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEALTH POLICY & SERVICES","Score":null,"Total":0}

引用次数: 0

Abstract

Muhammad Afzal, School of Computing and Digital Technology, Birmingham City University

[email protected]

Contemporary scientific communication relies heavily on document-based systems like journal articles, books, and reports for sharing research findings. However, large documents limit opportunities for efficient knowledge dissemination due to limitation in processing of different subsections within a document to understand the meaning of information units. This research aims to develop a smart repository that moves beyond documents and introduces smaller, computable units of knowledge. By assessing biomedical data sources, we will build a repository to make scientific knowledge more representable, computable, and shareable. The rationale is to enhance how researchers communicate and manage information in the rapidly evolving digital era.

The work focuses on developing a new repository that goes beyond the document-based paradigm by fusing biomedical and health and life sciences data sources, such as PubMed Central. New protocols and methods will be designed to identify relevant sections in the documents to extract smaller knowledge units. The proposed repository with key features storage, retrieval, representation, and sharing will be optimized for the granular units. Integration strategies with existing platforms like PubMed will be devised. Usability testing will refine the interface to boost engagement. Interoperability mechanisms will ensure compatibility with existing systems.

By enabling scientific knowledge to be shared in smaller units, this repository has the potential to revolutionize scientific communication and collaboration. Breaking down information into granular components is expected to create new opportunities for innovation, discovery, and the development of advanced analytics tools. The repository will facilitate efficient access to health evidence, benefiting researchers, clinicians in creating systematic reviewers that require rapid evidence synthesis. Further, the computable units extracted from documents could be modeled into interoperable resources like FHIR, thereby support the Evidence Based Medicine on FHIR (EBMonFHIR) project is extending FHIR to provide a standard for machine-interpretable exchange of scientific knowledge. This would also allow developers to build innovative AI systems for objectives such as diagnostic and treatment support.

By reducing the need for manual effort in finding and formatting evidence, the repository will pave the way for automating knowledge synthesis and management and will empower various stakeholders with enhanced efficiency, interoperability, and analytical capabilities to progress research and practice.

Miguel Aljibe, University of the Philippines

[email protected]

Alvin Marcelo, University of the Philippines-Manila

[email protected]

Janus Ong, University of the Philippines-Manila

[email protected]

Geohari Hamoy

[email protected]

The World Health Organization released the SMART Guidelines to advise member countries on a systematic approach to converting narrative policies into machine-readable formats. In the Philippines, a group of researchers attempted to apply the guidelines to the national viral hepatitis program. Several lessons were learned. First, government sponsorship is crucial at the outset as the effort of conversion can be substantial and confidence that the output will eventually be promulgated is essential. Second, consensus building is important to ensure that all stakeholders have been consulted and that the semantics are understood and accepted by all. Third, international standards such as HL7 FHIR and SNOMED play important roles. They must be made available for all to use. Lastly, constant communications among stakeholders (see consensus building in #2) will enable data exchange because of the trust built with open channels.

Ravi Bajra Bajracharya, CEO/Knowledge graph engineer, datum.md

[email protected]

Knowledge graphs are formal representation of knowledge bases as a network of nodes and edges where nodes represent biomedical concepts and edges link related nodes together to encode relationships between concepts in a knowledge base. A semantic knowledge graph treats each node as a resource with a unique URI and a semantic namespace and thereby normalizes concepts to similar semantic space across various sources of knowledge or information.

The primitives in our case can be health data standards, ontologies, terminologies, and vocabularies, which can be enriched using both structured and unstructured knowledge bases ranging from basic science, genomics data to clinical longitudinal records and population health demographics to community, environmental and behavioral research data.

The use of semantic data representation in a knowledge graph platform has proven to expand the use cases of the represented knowledge base to broader applications in the areas of analytics and prediction promoting reuse and interoperability of underlying biomedical knowledge sources involved.

Neha Bhasin, MD, MS, University of San Francisco

[email protected]

William J. Bosl, Digital Health Informatics Program, University of San Francisco, Center for AI and Medicine, University of San Francisco, Computational Health Informatics Program, Boston Children's Hospital, Harvard Medical School

[email protected]

Introduction: Psychiatric disorders impose an enormous burden of disease on all populations of the world, a burden that is likely underestimated because of a failure to appreciate the interconnectedness of mental and physical health. While research in basic neuroscience has advanced considerably, translation of new knowledge into new treatments has been slow. Mental disorders generally emerge over time due to complex factors, creating opportunities for early intervention to redirect the developmental trajectories and to monitor the efficacy of treatments. Unfortunately, the data necessary to monitor neurodevelopmental trajectories relevant to psychiatric disorders are typically not collected in routine clinical care. Further, research to develop computational algorithms to model disease trajectories depends on these data that are not generally available from clinical sources. We propose creation of a new paradigm for a learning mental health system that is designed around a globally accessible personal psychiatric database that will support research in computational psychiatry and will evolve into clinical decision support systems.

Objective: The primary goal for this research is to create a new paradigm to collect mental health relevant data to develop algorithms for monitoring neurodevelopmental trajectories to enable early risk assessment and monitoring for mental disorders.

Methods: Our team and others are developing computational approaches that enable latent information from complex, multimodal data, including EEG as a functional brain measurement, to be extracted. Importantly, clinical validation of these computational methods will require standardized datasets from large, diverse populations. We are implementing an open and secure platform for brain specific data using open (FHIR) APIs that will enable this data to be shared with institutionally based EHRs. Our goal is to create a globally accessible cloud-based personal mental health record for research in computational psychiatry that also seeks to create a brain-health monitoring clinical paradigm. Consent for running algorithms on this data in a federated fashion will be mandatory. Our methodology integrates three components to create a computable mental health knowledge system: a mobile platform to collect EEG and other relevant data outside of the clinic, a FHIR-based personal mental health database, and algorithms to compute neurodevelopmental risk trajectories from the database.

Discussion: Our previous research has recruited research cohorts to identify biomarkers for neurodevelopmental disorders in children from EEG recordings. To transition this work from the laboratory to practice, longitudinal data is needed for clinical validation studies. These data are not typically collected in routine pediatric checkups, necessitating a new paradigm for acquiring the relevant data. We are proposing a new kind of learning health system in which the need for ecologically valid research data will drive the creation of a new patient-centric approach to mental health monitoring. Our initial focus will be on neurodevelopmental disorders in children.

Conclusion: Care for brain disorders may be advanced by patient-centered longitudinal research that implements Computable Biopsychosocial Knowledge for psychiatry through FHIR-based, brain-specific personal health record systems, together with algorithms tested and developed on that database. An initial pilot implementation for childhood neurodevelopmental disorders is being implemented to demonstrate the system.

Peter Boisvert, Department of Learning Health Sciences, University of Michigan

[email protected]

Marisa Conte, Department of Learning Health Sciences, University of Michigan

[email protected]

Allen Flynn, Department of Learning Health Sciences, University of Michigan

[email protected]

Charles P Friedman, Department of Learning Health Sciences, University of Michigan—on behalf of the Knowledge Systems Lab

[email protected]

Metadata is essential to achieve FAIR (Findable, Accessible, Interoperable and Reusable) computable biomedical knowledge (CBK). The Mobilizing Computable Biomedical Knowledge (MCBK) community has defined categories of metadata^[1] and supports ongoing efforts to develop a minimal metadata model for CBK. Complementing this work, we recognize an emerging role for extensible metadata, which can be generated by both human- and non-human actors throughout the lifecycle of a CBK artifact.

Technical methods for enabling extensible metadata are well-known, including Protégé for ontology creation and management, and the CEDAR workbench for authoring metadata. Existing domain and infrastructural metadata can capture various perspectives on CBK artifacts or collections, including domain-specific (Human Phenotype Ontology) or provenance (PROV-O) ontologies, technology-specific metadata (Maven coordinates), and so on. In a linked-data world, these should all be linkable and interoperable in the context of a particular instance or class of CBK, and its stakeholders.

Trust in CBK is essential for its use, dissemination, and reuse. Trust is also essentially a function of networks and community, not of a CBK artifact itself. Extensibility allows communities to enrich metadata with elements that are essential for trust by that community, increasing the potential for increased implementation and use.

Ongoing efforts to develop standards and models for CBK metadata should also include ways to promote extensible metadata. Extensibility acknowledges the plasticity of both CBK and its uses/users, and the enriched metadata provided by diverse communities or other agents may increase the adoption, usefulness, and reusability of CBK.

¹ Alper BS, Flynn A, Bray BE, Conte ML, Eldredge C, Gold S, et al. Categorizing metadata to help mobilize computable biomedical knowledge. Learn Health Syst 2021;n/a(n/a):e10271.

Ruth Carlyle, NHS England

[email protected]

Application and use of computable biomedical knowledge depends upon digital skills and health literacy, the ability to access, assess and use health information.

This lightning talk will share data from England on variations in health literacy and digital connectivity. At a national level, 43% adults aged 16–25 struggle to understand health information that uses words. When numbers are added, 61% adults struggle—as most health information in practice combines words and numbers, this means that the majority of the population struggle even when health information is in print (Rowlands et al 2015). If members of the public are to contribute to and benefit from the personalisation of health information made possible by computable biomedical knowledge, they need to understand the information and to trust both the sources and the usage.

The national NHS Knowledge and Library Services team (now part of NHS England) provides strategic leadership for NHS knowledge and library services in England. The aim in its strategy is that ‘NHS bodies, their staff, learners, patients and the public use the right knowledge and evidence, at the right time, in the right place, enabling high quality decision-making, learning, research and innovation, to achieve excellent healthcare and health improvement’ (Knowledge for Healthcare, 2021). As part of delivering this aim, we have a workstream on health literacy and patient information, developing national partnerships and creating and spreading tools.

As a tool to understand health literacy, the team commissioned the University of Southampton to reanalyze literacy and numeracy data as geodata, to show local variation. This lightning talk will give a brief insight into both health literacy geodata and mapping of digital connectivity, with its implications for computable biomedical knowledge.

The talk will also introduce a national partnership with CILIP (the professional body for librarians and information professionals in the UK), Libraries Connected (overseeing public/community libraries) and Arts Council England. Through the partnership, community-based public libraries and prison libraries have worked with members of the public to increase confidence in health literacy and provide skills and digital hubs to interact with health information in digital forms. Learnings from this activity demonstrate the role that libraries and library staff in health and community settings can provide in increasing the health literacy and digital literacy for the public on which use of computable biomedical knowledge depends.

Shuaipu Chen, School of Information Management, Wuhan University, Institute of Big Data, Wuhan University

[email protected]

Yuxing Qian, School of Information Management, Wuhan University, Institute of Big Data, Wuhan University

[email protected]

Zhenghao Liu, School of Information Management, Wuhan University, Institute of Big Data, Wuhan University

[email protected]

In the application of the Data-Information-Knowledge-Wisdom model in biomedical and health sciences, the computation of the value of knowledge becomes pivotal in empowering decision-making as a tangible manifestation of wisdom.

We propose a specific method for knowledge computation, drawing from both metaknowledge theory and scenario theory. Metaknowledge theory highlights the value of knowledge not only in its content but also in its cognitive states. To address the complexity of knowledge representation, we first redesign the organizational form of knowledge content, as the traditional triplet format proves inadequate. Consequently, we introduce the concept of knowledge scenario based on scenario theory, intending to enrich knowledge with additional attributes beyond the traditional triplet format. These attributes are represented as scenario attributes that encompass both physical and information spaces, emerging through the process of knowledge generation and application. This step accentuates the computation of knowledge content applicability. Moving forward, we proceed to compute knowledge cognitive states by analyzing the certainty strength of the knowledge itself and the supporting strength of external evidence. Through the integration of these two aspects, we successfully achieve knowledge computation.

To verify the effectiveness of our proposed method, we conduct two experiments. Firstly, we apply the method to biomedical and health sciences, specifically focusing on Mild Cognitive Impairment (MCI). We construct a scenario-based knowledge graph and establish rules to explore the multi-angle knowledge association characteristics of fusibility, inheritance, and inference. By retrieving knowledge based on scenario matching, we significantly enhance the applicability of the knowledge. Secondly, we employ ChatGPT, a representative large language model, and integrate our method as a prompting engineering following a specific chain of thought. By applying this approach to the diagnosis of MCI, we effectively mitigate the occurrence of hallucinations in the large language model, substantially improving the reliability of knowledge.

Joanne Dehnbostel, MS, MPH, Computable Publishing LLC, Scientific Knowledge Accelerator Foundation

[email protected]

Brian S. Alper, MD, MSPH, Computable Publishing LLC, Scientific Knowledge Accelerator Foundation

[email protected]

Khalid Shahin, BA, Computable Publishing LLC, Scientific Knowledge Accelerator Foundation

[email protected]

Joshua Richardson, PhD, MS, MLIS, FAMIA, RTI International

[email protected]

Standard terminologies facilitate unambiguous communication in many domains. However, there are no globally accepted and adopted standard terminologies for reporting scientific knowledge. A global effort started in 2020 to define standard terminologies (in the form of code systems) for four scientific knowledge concepts: Study Design, Risk of Bias, Statistic Type, and Statistical Model.

The effort created a Code System Development Protocol to support global development of terminologies for exchanging scientific evidence. Initial steps of the protocol included (1) assembling expert working groups with people from more than 25 countries, (2) identifying 23 commonly used tools and systems for which standard terminology would be useful, (3) drafting 368 non-redundant concepts to become display terms for the four code systems, (4) identifying 27 ontologies with related terms and definitions, and (5) mapping available terms and definitions for the draft concepts. (Alper BS, Dehnbostel J, Afzal M, Subbian V, Soares A, Kunnamo I, Shahin K, McClure RC, For the COVID-19 Knowledge Accelerator (COKA) Initiative. Making Science Computable: Developing code systems for statistics, study design, and risk of bias. Journal of Biomedical Informatics 2021 Mar;115:103685. https://doi.org/10.1016/j.jbi.2021.103685).

We consolidated these efforts into a single Scientific Evidence Code System (SEVCO), developed tooling to facilitate the work (including FEvIR®: CodeSystem Builder/Viewer and FEvIR®: My Ballot), and streamlined the protocol to enable more efficient development processes (Alper BS, Dehnbostel J, Lehmann H, Whaley P, Wilkins KJ, Tufte J, Yurk RA, Ojha N, Afzal M. For the COVID-19 Knowledge Accelerator (COKA) Initiative. Scientific Evidence Code System Development Protocol. Created November 16, 2021. Last revised December 8, 2021. Available at: https://tinyurl.com/SEVCOprotocol).

The SEVCO development is available for open viewing at. https://fevir.net/resources/CodeSystem/27270#TOP and anyone can comment on any term. Participation in terminology deliberations is also open to anyone. You can self-select to join the SEVCO Expert Working Group at https://fevir.net/resources/Project/27845 and as terms are open for voting, you can vote Yes or No and/or provide comments for what changes may be needed. Terms are passed when they receive 100% agreement with at least five votes, and negative votes lead to discussion and modification until they reach approval.

As of July 18, 2023, SEVCO has 595 terms, of which 342 (57.5%) have unanimous approval.

Once completed, the code system should more effectively facilitate identifying, processing, and reporting research results and the reliability of those results. More efficient and detailed scientific communication will reduce cost and burden and improve health outcomes, quality of life, and patient, caregiver, and healthcare professional satisfaction.

SEVCO is developed by the Health Evidence Knowledge Accelerator (HEvKA), which is an open, virtual group to accelerate identifying, processing, and disseminating computable biomedical knowledge, especially related to clinical research. HEvKA efforts inform and develop standards, terminologies, and tools for computable expression of evidence and guidance. You can participate through any of 15 open virtual weekly meetings. Details at https://tinyurl.com/HEvKA

Allen Flynn, University of Michigan Medical School and School of Information

[email protected]

What rights should CBK creators and Metadata Contributors enjoy, and how will those rights be upheld? With written knowledge production, journals identify and authenticate authors, uphold standards via peer review, and maintain rights. Namely, via journals, rights of attribution and integrity of authors' works are upheld, incentivizing publication.

To have a trusted and economically viable online CBK ecosystem, identification, authentication, and attribution (IAA) of all creators and contributors is required. Yet the online world that has evolved to date fosters anonymity, not IAA. In his 2016 piece “How to fix the internet”¹, Walter Isaacson prescribed changes to counter anonymous malicious online actors. We apply Isaacson's ideas to envision a trusted online CBK ecosystem that incentivizes voluntary, verifiable IAA.

Voluntary, verifiable IAA enables creators of CBK artifacts and metadata to benefit when others use what they produce. By upholding creators' rights of attribution and the integrity of CBK works, creators can benefit via recognition and payments. In addition, for economic viability to be achieved, these creators must be shielded from liability that cancels out such benefits. Only when creators can gain from participating in a future CBK ecosystem should we expect routine compliance with voluntary, verifiable IAA to follow.

We imagine these five items are necessary, if not sufficient, to establish suitable economic incentives for any viable future CBK ecosystem.

¹ Isaacson W. How to fix the internet. The Atlantic. 2016 Dec;15.

David Hanauer, University of Michigan

[email protected]

Lisa Ferguson, University of Michigan

[email protected]

Kellen McClain, University of Michigan

[email protected]

Guan Wang, University of Michigan

[email protected]

Approaches for achieving computable biomedical knowledge (CBK) in the clinical domain often require the use of unstructured (free-text) clinical notes from electronic health records (EHRs). There remain significant challenges for broad use of unstructured clinical data, in part because many natural language processing (NLP) and text retrieval/processing systems are too complex to use for non-technical users. Our team has been developing tools for enabling non-technical research and operational teams within healthcare settings to securely access and utilize unstructured EHR notes for a variety of purposes ranging from clinical research to quality improvement in healthcare systems.

EMERSE—the electronic medical record search engine—is a text retrieval and text processing system to help “democratize” the use of free text data in EHR notes, with a special emphasis on usability for non-technical users. EMERSE has many features specifically designed to support biomedical research, including robust query expansion that can leverage similar terms from multiple vocabularies and ontologies (e.g., Human Phenotype Ontology, Orphanet Rare Diseases, the Gender, Sex, and Sexual Orientation Ontology, and more). EMERSE securely enables network-based searches to obtain obfuscated patient counts across participating institutions. EMERSE also supports collaboration and re used of search terms through a feature in which collections of terms/phrases can be shared and re-used by other teams. EMERSE also groups notes by patients, making it easier to identify cohorts for tasks such as eligibility determination for clinical trials.

New development work underway includes the additional of named entity recognition (NER) and coding notes to the Unified Medical Language System (UMLS) using Concept Unique Identifiers (CUIs). Such an approach will allow users to search for terms and concepts interchangeably. The ability to consider negation status, uncertainty, and whether the text is about the patient or another individual is also planned. EMERSE has been in constant development since 2005 and is now operational at multiple large academic medical centers across the United States and Europe. It is available at no-cost under and open source license.

Jianguo Hao, National Institute of Health Data Science, Peking University, Institute of Medical Technology, Health Science Center of Peking University

[email protected]

Shichao Fang, King's College London

[email protected]

Qing Li, Advanced Institute of Information Technology, Peking University

[email protected]

Guilan Kong, National Institute of Health Data Science, Peking University, Institute of Medical Technology, Health Science Center of Peking University, Advanced Institute of Information Technology, Peking University

[email protected]

Radiological examination is an effective and valuable method for thoroughly inspecting a patient's chest. Clinical data, which can provide valuable supervisory information for machine learning algorithms, is commonly stored in electronic health records (EHRs). Chest radiographs often contain multiple pathologies, making the diagnosis of these multi-label medical images without precise annotations particularly challenging and time-consuming. This study endeavored to combine a hierarchical clustering method called formal concept analysis (FCA) and a contrastive learning paradigm to learn enhanced discriminative representations from EHR data and chest x-ray radiographs (CXRs), with the aim of offering support for downstream tasks, such as similar image retrievals.

The data source utilized in this study is the Medical Information Mart for intensive care chest x-ray (MIMIC-CXR) dataset, known as one of the largest de-identified publicly accessible repositories of CXRs for clinical data analysis. Its de-identified structured EHR data contains demographic information, concise clinical interpretations, and meta-data such as orientations of CXRs. It should be noted that each patient may undergo multiple radiological examinations, with each preliminary diagnosis being documented in a free-text radiology report.

To encode CXRs, we leveraged a deep learning model, EfficientNet, as the backbone network. The model was initialized with pretrained weights obtained from the ImageNet dataset and then fine-tuned for transfer learning using contrastive learning to adapt the MIMIC-CXR dataset. The model further incorporated a pretext task employing a hierarchical clustering algorithm to cluster CXRs. Categorical EHR data extracted from free-text reports, such as pathologies or CXR findings were served as a source of supervisory information. The MIMIC-CXR dataset was split into training, test, and validation subsets. Subsequently, FCA approach generated a hierarchical clustering graph that clustered the CXRs based on various sets of supervisory information. In the generated graph, clusters were systematically organized and interconnected based on their mutual correlations of supervisory information. It could facilitate a coherent representation of relationships and dependencies among the clustered CXRs, providing valuable insights into their similarities and dissimilarities. Given an anchor CXR, the graph classified indexed CXRs as either positive or negative cases by considering the relationship between their supervisory information. To develop the instance discrimination model using contrastive learning, triples consisting of anchor CXRs together with their corresponding positive and negative CXRs, were formed in the training process. These encoded triples were designed to continuously optimize the model by a contrastive loss to get similar CXRs closer and pull away dissimilar ones. This optimization process made the learned representation space of CXRs more discriminative. Compared to self-supervised contrastive learning, the proposed hierarchical contrastive learning performed better in the downstream task of cross-modal retrieval for similar cases. The average retrieving precision of the top 5 retrieved similar cases increased from 76.80% to 81.01%.

To summarize, we proposed a supervised hierarchical contrastive learning approach to learn discriminative representations from multi-label CXRs. By combining contrastive learning with hierarchical clustering, the proposed model reinforces the hierarchical supervision and makes all the studied cases well represented, and thus enables better discrimination of multi-label CXRs.”

Xia Jing, Clemson University

[email protected]

Richard D. Boyce, University of Pittsburgh

[email protected]

Hua Min, George Mason University

[email protected]

Yang Gong, University of Texas Health Science Center at Houston

[email protected]

James J. Cimino, the University of Alabama at Birmingham

[email protected]

Dean F. Sittig, University of Texas Health Sciences Center at Houston

[email protected]

Clinical decision support systems (CDSS) have played a critical role in delivering safe, efficient, and quality clinical care. Rule-based CDSS have a long history of use in large medical institutions because they need to maintain ever-evolving rules. However, sharing computable CDSS rules across institutions has yet become commonplace. Our international collaboration group aims to use ontology to develop computable CDSS rules, particularly for resource-limited primary care settings. The work could provide usable, maintainable, up-to-date, and computable CDSS rules for primary care settings that lack in-house IT professional support. We have used CDC-recommended vaccination schedules (≤18 years, 2022 version, five tables with annotations) as the starting CDSS rules.

Translating the vaccination recommendations (start point) directly into machine-processable formats (endpoint) is unrealistic. Several mediate layers are needed between the start and endpoints; for example, a detailed tabular format compiled from the current CDC vaccination recommendation schedules is required; then, a detailed and thorough version represented in formal language formats can be easily converted to different programming languages. We aim to use unambiguous formats to represent the human-readable CDSS rules from tabular formats to one that programmers can use to develop machine-processable formats accurately. This strategy also minimizes errors in generating the CDSS rules in different programming languages.

We have completed the first mediate layer with over 465 rules for 19 vaccines in tabular format. We are currently generating the second mediate layer: a detailed and accurate version of the CDSS rules in formal languages, such as Web ontology language (OWL), clinical quality language (CQL), or Drools. The formal language formats can be translated into programming languages based on the deployment system requirements. Meanwhile, we are constructing a CDSS ontology to organize the mapping between concepts and individual terms across terminologies, both of which are used in representing the CDSS rules in mediate and final versions. OWL can provide reasoning potential; however, there are limitations to representing the complex and exact logical expressions typical of CDSS rules. CQL seems more promising in representing logic expressions. However, downstream use needs further demonstrations. Drools has been used in generating business logic and rules; however, like OWL and CQL, there is a deep learning curve to overcome in using Drools and setting up the work environment.

Although there will be challenges along the way, this is a feasible pathway for generating computable CDSS rules, a critical milestone in achieving true interoperability. Converting the 5-table CDC vaccination recommendations into detailed tabular formats and then to CDSS rules in formal languages bridges human comprehensible representations and machine-processable formats with explicit, clear, accurate representations of CDSS rules. It is a step toward achieving truly interoperable patient records, a long-time dream yet to be realized in healthcare IT.

Inwon Kang, Rensselaer Polytechnic Institute

[email protected]

William van Woensel, University of Ottawa

[email protected]

Oshani Seneviratne, Rensselaer Polytechnic Institute

[email protected]

We explore using Large Language Models (LLMs) to generate computable knowledge in the form of decision logic (Notation3, Clinical Quality Language) and smart contracts (Solidity) that encode health insurance processes found in natural text. We present an initial methodology that generates output at increasing levels of complexity and technical detail: i.e., with generation tasks (1) structured summaries, (2) formalized decision logic, and (3) smart contract code. The LLM's output at each level can support a domain expert or Web3 developer in authoring decision logic or smart contracts. We engineered our LLM prompts following best practice guides provided by OpenAI and Deep learning AI. We propose experiment metrics, including completeness, soundness, clarity, and syntax, to evaluate the LLM's output. Our evaluation employs three textual scenarios on health insurance processes, progressively increasing in difficulty in length and comprehensibility for non-domain experts, sourced from Medicare's official booklet. Our findings indicate that the LLM performs well in generating structured textual summaries across all scenarios. However, for tasks (2) and (3) that generate decision logic and runnable smart contracts, implementing particular health insurance processes, we found that the model's output requires human oversight to ensure validity. Notably, the model does not apply basic conceptual modeling principles, meaning that when runnable, the generated code will not be sound (i.e., yielding false positives or negatives). Moreover, the most advanced scenario seems too complex for the LLM to generate a correct set of decision logic and code, with more complex conditions being outputted as natural language comments instead of code, and other conditions simply missing. However, one of our decision logic targets, Clinical Quality Language, has very poor syntax for all scenarios, likely due to the lack of online training data. Nevertheless, our experiments demonstrate the promise of LLMs in supporting the translation of process descriptions from dense natural text into formal decision logic and smart contracts, which may aid streamlining many complex processes.

Rami Khoury, MD, American College of Emergency Physicians Board Member, Independent Emergency Physicians-PC

[email protected]

Pawan Goyal, MD, MHA, American College of Emergency Physicians

[email protected]

Dhruv Sharma, BA, MS, American College of Emergency Physicians

[email protected]

Data is driving the future of medicine. We've already seen the critical importance of real-time insights to new and emerging health threats during the COVID-19 pandemic, as well as the impact of health care trends and patterns of resource utilization. With the new Emergency Medicine Data Institute (EMDI), the American College of Emergency Physicians (ACEP) is rapidly moving emergency medicine to the forefront of data-driven quality and practice innovation. This new initiative is poised to become a source of truth for all emergency medicine data. Harnessing the power of information that physicians are already recording, ACEP collates vital metrics from emergency departments nationwide to support research and grants, while enhancing value for emergency physicians, patients, and the broader health care community. The presenters will enumerate the current, state-of-the-art, and future challenges facing emergency medicine that will be addressed by the EMDI, illuminated with examples of data use from other medical specialties and early success stories. Attendees will be given a sense of emerging trends in clinical, quality, and economic aspects of emergency care that demonstrate the value and power the EMDI brings to the specialty.

Guilan Kong, National Institute of Health Data Science at Peking University, Advanced Institute of Information Technology, Peking Universit.

[email protected]

Jiayu Wang, Institute of Medical Technology, Peking University Health Science Center

[email protected]

Bixia Gao, Renal Division, Department of Medicine, Peking University First Hospital

[email protected]

Jinwei Wang, Renal Division, Department of Medicine, Peking University First Hospital

[email protected]

Luxia Zhang, National Institute of Health Data Science, Peking University

[email protected]

Minchao Liu, Department of Computer Application and Management, Chinese PLA General Hospital

[email protected]

Diabetes is a serious public health problem around the world. Nowadays, the prevalence of diabetes in low- and middle- income countries (LMIC) has been rising, and the increase of disease burden of diabetes in LMIC is faster than high income countries. Moreover, various types of complications may come with diabetes, and thus a heavy economic burden has been put on both patient families and the society. Conventionally, diabetes was classified into two types—Type 1 and Type 2—which are also known as “Insulin Dependent” and “Noninsulin Dependent” diabetes, respectively. The treatment methods for Type 1 and Type 2 diabetes are different accordingly. In practice, due to the high degree of heterogeneity among diabetes, the clinical characteristics and outcomes of Type 2 diabetes patients are very different as well.

With the advances of big data, the concept of computable phenotypes was proposed to classify patients into different subtypes based on clinical data characteristics. The patients belonging to the same disease subtype may have similar complication risk, and the treatment strategies of patients with common clinical characteristics may be similar.

In diabetes, a data-driven subgroup analysis was conducted on a Swiss diabetes cohort in 2018, and five subtypes were identified for diabetes patients based on six clinical variables, including glutamate decarboxylase antibodies, age at diagnosis, body mass index (BMI), HbA1c, and homoeostatic model assessment 2 estimates of β-cell function (HOMA2-B) and insulin resistance (HOMA2-IR). Each subtype has different patterns in complication occurrence. Several studies have been conducted among different ethnicities around the world to validate the existence of the identified 5 subtypes of diabetes.

In this study, the clinical data of diabetes patients going to the clinic of or being admitted to a Grade A Class 3 hospital in Beijing from 2000 to 2020 was used as data source. As glutamate decarboxylase antibodies is a clinical variable related to Type 1 diabetes and rarely recorded in the electronic medical records (EMR) system, diabetes patients with the data of five clinical variables (age, BMI, HbA1c, HOMA2-B and HOMA2-IR) recorded around the time of diabetes diagnosis were included for analysis. According to the central point value of each clinical variable in the five subtypes identified by the Swiss study, the distance between the clinical vector (age, BMI, HbA1c, HOMA2-B and HOMA2-IR) of each patient and the central point vector of each subtype was computed, and then each patient was classified into the subtype with the shortest distance. Therefore, each patient included for analysis was assigned a subtype based on his or her clinical characteristics at diabetes diagnosis. Finally, the patterns of diabetic complication occurrence were analyzed for the studied diabetes patients, and similar disease progression patterns have been found for patients labeled with the same subtype.

The computable phenotyping tool developed in this study had the capability to aid physicians to grouping Type 2 diabetes patients, and thus has the potential to facilitate precision and personalized medicine. Ideally, a recommendation of clinical treatment methods can be provided for each diabetes subtype after further analysis.

Zach Landis-Lewis, University of Michigan

[email protected]

Allen Flynn, University of Michigan

[email protected]

Peter Boisvert, University of Michigan

[email protected]

Hana Chung, University of Michigan

[email protected]

Patrick Galante, University of Michigan

[email protected]

Ayshwarya Jagadeesan, University of Michigan

[email protected]

Mobilizing CBK requires that its implementers and key stakeholders efficiently develop an understanding of its purpose and function. However, supporting learning about CBK can be challenging due to the complex and abstract nature of algorithms, knowledge graphs, and computational models. Furthermore, when diverse models are implemented in software applications, the effort required to learn about a model collection can become prohibitively high. Methods to better support learning about CBK for its successful mobilization are needed.

Vignettes are tutorial-like technical documentation, written for learning about R statistical software packages. Vignettes make complex software easier to understand by providing examples of data and analyses with supporting discussion and interpretation. To our knowledge, a practice like that of developing vignettes for CBK has not been widely adopted, yet writing vignettes may benefit CBK developers, implementers, and stakeholders in promoting more efficient learning about models and model collections.

We have developed 10 vignettes for a model collection in a precision feedback system. The system, which has been implemented for anesthesia providers, prioritizes motivational feedback messages about care quality based on the preferences of feedback recipients. The model collection includes logic models, ontologies, and algorithms which interact through modules of a software pipeline, implemented in a web service to support clinical quality dashboard and email content.

Each vignette is implemented as a readme file in a public GitHub repository. We developed each vignette with two characters (i.e., personas) whose differing preferences result in the selection of different messages by the precision feedback system. The vignettes describe system components and models, and include links to external resources, such as ontology terms and classes in BioPortal.

Through the vignette development process, we have begun to recognize multiple types of value. Our driving purpose has been to provide an accessible resource for learning about the precision feedback system by system implementers and stakeholders. However, the process of writing the vignettes has resulted in enhanced team communication and improved organization of system development tasks.

Furthermore, we have begun using the vignettes to guide system testing activities, including optimizing system performance. As a result of these insights, we plan to develop the vignettes as documentation that is packaged with the model collection and for future software releases of the precision feedback system.

Wentie Liu, National Institute of Health Data Science, Peking University

[email protected]

Tongyue Shi, National Institute of Health Data Science, Peking University

[email protected]

Shuai Jin, Department of Adult Care, School of Nursing, Capital Medical University

[email protected]

Jianguo Hao, National Institute of Health Data Science, Peking University, Institute of Medical Technology, Health Science Center of Peking University

[email protected]

Huiying Zhao, Peking University People's Hospital

[email protected]

Guilan Kong, National Institute of Health Data Science at Peking University, Advanced Institute of Information Technology, Peking University

[email protected]

Sepsis is life-threatening organ dysfunction caused by a dysregulated host response to infection. It leads to high in-hospital mortality, particularly for intensive care unit (ICU) patients. Identifying septic patients who are at high risk of in-hospital mortality early on can aid ICU physicians in making optimal clinical decisions. This study aimed to develop machine learning-based tools that can predict hospital mortality risk for septic patients in the ICU based on clinical data gathered in the first 3 hr of sepsis.

Fluid therapy represents a clinical treatment approach which focuses on maintaining fluid homeostasis through the supplementation or restriction of specific fluids. As a life-threatening condition, early resuscitation volume is important for sepsis patients, and it impacts patient prognosis and outcomes. Regrettably, most existing predictive models for sepsis mortality have not incorporated the early resuscitation volume for analysis. In clinical practice, early fluid resuscitation is advocated for sepsis patients. The 2016 Surviving Sepsis Campaign guideline recommends that at least 30 mL/kg of intravenous (IV) crystalloid fluid should be given within the first 3 h of resuscitation to patients with sepsis. As can be seen from the guideline, the initial 3 h following the diagnosis of sepsis are considered as the critical “golden time” for early resuscitation. Therefore, in this study, the intervention of early resuscitation was incorporated in mortality risk prediction modeling. A comprehensive set of clinical variables, which were collected in the first 3 h of sepsis diagnosis, together with the volume of crystalloid fluid administered during this initial 3 h were included for analysis.

The Medical Information Mart for Intensive Care-IV(MIMIC-IV) database containing records of over 40 000 ICU patients admitted to the ICUs at Beth Israel Deaconess Medical Center between 2008 and 2019, was used as data source. The sepsis patient data extracted from MIMIC-IV formed a large study population and the clinical information contained including demographics, lab tests, clinical assessments and medical treatments. Around 80% of sepsis data was used for model development and the remaining for model test.

In terms of analytics, several machine learning methods with good explainability, which have also demonstrated satisfied predictive capabilities in medicine, including Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), together with multivariate logistic regression were used in this study. Finally, prediction performance of two machine learning-based models was compared with that of the traditional logistic regression, and the prediction model with best performance was selected for clinical recommendation.

The predictive tool developed in this study will help to identify sepsis patients with high in-hospital mortality risk at an early stage. Hopefully, it will aid ICU physicians to provide timely and optimal interventions, and thus may help to improve ICU patient outcomes and reduce in-hospital mortality.

Joshua Martinez, Stanford University

[email protected]

Cataracts are the leading cause of vision impairment globally, and cataract surgery is the most commonly performed surgery in the United States. Continuously enhancing surgical training is key for ensuring high surgical standards and maintaining excellent visual outcomes of surgery. Traditionally, surgical training and feedback is based almost solely on preceptor-led real-time feedback in the operating room. The advent of machine learning, specifically deep learning models, has the potential to augment surgical feedback by enabling more granular and highly objective analysis of surgical performance through analysis of routinely captured surgical video. In previous work, we developed deep learning models that could identify key surgical landmarks and the surgical steps being performed in cataract surgery videos, thereby offering a novel means of quantitatively assessing surgical skill. We present here an implementation of these models into an application called PhacoTrainer, which is a platform designed to provide cataract surgeons with objective, automated feedback on their surgical techniques, and to facilitate future research on cataract surgery.

The PhacoTrainer platform is a web-based application to which users can upload cataract surgical video and receive insights into their cataract surgical performance. The platform deploys a deep learning model, a hybrid Convolutional Neural Network and Recurrent Neural Network, to uploaded videos to detect which surgeries include special surgical techniques or complications. The model outputs also enable calculation of the time spent on each step of surgery, which then is displayed in a dashboard visualizing the change in surgical times for each step as a surgeon accumulates more experience. The timeline of each surgical video is also automatically annotated with a frame-by-frame determination of which surgical step is being performed, to better allow surgeons to browse through surgical video. Thus, the feedback provided by PhacoTrainer based on these models equips surgeons with insightful metrics to monitor their surgical performance across multiple dimensions, identifying areas for potential improvement.

The PhacoTrainer platform heralds a significant advancement in cataract surgery training and transforms unstructured cataract surgical video into computable insights. By mobilizing deep learning to objectively analyze surgical videos, it provides surgeons with a tool to self-evaluate their skill, track improvements, record surgical metadata, and ultimately enhance surgical outcomes. PhacoTrainer also makes high quality feedback available to all trainees, irrespective of geographical or institutional constraints. With its capability to accumulate a vast repository of metadata on cataract surgery, PhacoTrainer also promises to catalyze future research on cataract surgery, facilitating a more nuanced understanding of surgical techniques over time and cataract surgical training.”

Alan H. Morris, MD, University of Utah

[email protected]

For the replicable decision-support group.

Clinical decision-making is based on knowledge, expertise, and authority, with clinicians approving almost every intervention according to the Hippocratic model of clinical decision-making. This is the starting point for delivery of “All the right care, but only the right care,” a quality goal yet unachieved because unaided clinicians suffer from human cognitive limitations and biases when decisions are based only on their training, expertise, and experience. Robust decision-support tools that reduce unwarranted variation of clinician decisions and actions can improve healthcare. Current electronic health records (EHRs) are focused on results review, documentation, and accounting. EHRs are awkward, time consuming, and contribute to clinician stress and burnout. Decision-support tools can reduce clinician burden and enable replicable clinician decisions and actions that personalize patient care. However, most current clinical decision support tools/aids lack detail and neither reduce clinician burden nor enable replicable clinician actions. Clinicians must provide subjective interpretation and missing logic, thus introducing personal biases and mindless, unwarranted, variation from evidence-based practice. Replicability occurs when different clinicians, with the same patient information and context, come to the same decision and action. A subset of therapeutic decision support tools based on credible clinical outcome evidence exists as computer protocols (eActions), including closed-loop systems, that lead to replicable clinician actions. eActions enable different clinicians to make consistent decisions and actions when faced with the same patient input data and context. In advanced modern healthcare delivery environments, eActions have overcome cognitive limitations of overburdened clinicians. eActions embrace good everyday decision-making informed by evidence, experience, EHR data, and individual patient status. eActions can reduce unwarranted clinician variation, increase quality of clinical care and research, reduce EHR noise, and could enable a learning healthcare system.

Delivering best evidence-care remains a vexing problem that is achieved only ~50% of the time in advanced healthcare systems. Evidence-based guidelines can address only a small fraction of the types of clinical care. Underserved areas rarely can access state-of-the-art evidence-based guidelines in real time, and often cannot implement advanced guidelines. Care providers in such settings frequently do not have sufficient training or time to implement advanced guidelines. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems that could yield new or modified evidence-based guidelines derived from comparative effectiveness clinical research carried out during routine healthcare delivery within true learning healthcare systems. Developing countries could likely benefit as well from focused applications using limited healthcare electronic systems and smart phones.

Katelin Morrissette, University of Vermont Medical Center

[email protected]

Many important components of medical decision making, such as diagnostic certainty, interventions considered but avoided, or patient input in the management decisions are challenging to measure from existing data elements in the medical record. We present a method to build custom data elements to reflect these components of medical management and describe the implementation process. New innovations in medical management may not be represented in traditional elements of the electronic health record and will rely on these customized data elements as well. For example, in critical care medicine the phase of care before a patient is admitted to an intensive care unit (ICU) can be thought of as peri-ICU. This peri-ICU phase is when interventions which may avert an ICU admission, or define initial diagnosis and management occur. We have used features of the Epic EHR to create computable knowledge customized to this specific phase of care and a critical care consult service. The consult service bridges hospital departments, utilizes tele-health, and seeks to treat critical illness outside the walls of an ICU. It was important to understand the impact on resource utilization as well as medical decision making and required customized data to be easily reported, shared, and improved over time. We use this case example to demonstrate feasibility and the implementation process for how computable data elements can be customized for specific clinical scenarios, rapidly deployed in line with clinical needs, and has potential to stimulate data sharing for concepts that were previously ambiguous and difficult to quantify.

Jerome A. Osheroff, TMIT Consulting, LLC and University of Utah Department of Biomedical Informatics and Department of Veterans Affairs

[email protected]

Brian S. Alper, Computable Publishing LLC

[email protected]

Philip Barrison, University of Michigan Medical School Department of Learning Health Sciences

[email protected]

Mary Butler, University of Minnesota

[email protected]

Joshua E. Richardson, RTI International

[email protected]

Ken Rubin, University of Utah Department of Biomedical Informatics; Department of Veterans Affairs

[email protected]

Janice Tufte, Hasannah Consulting

[email protected]

for the Pain/Opioid LHS Learning Community https://docs.google.com/document/d/1J_Td8uQsXi9p0HWzDGsRmI7Dg3x-FBo3BWtzN5k-Pe8/edit?pli=1

Healthcare is far from realizing the Quintuple Aim of optimizing health at reduced costs while increasing equity and patient and clinician satisfaction. Aspiring Learning Health Systems (LHSs) are attempting to close this gap by enhancing the cycle whereby healthcare evidence is used to guide care—and care delivery results are used to continually improve available evidence. Interoperable, computable biomedical knowledge (CBK) is essential to optimize this cycle.

From 2018 to 2021, the AHRQ Evidence-based care Transformation Support initiative (ACTS) engaged over 330 individuals from diverse stakeholder groups to produce a stakeholder-driven future vision and action plan—and conduct pilots and concept demonstrations—for a knowledge ecosystem that supports CBK-enabled LHS cycles. After ACTS, the project lead (Osheroff) formed the LHS Collaborative to drive stakeholder-supported efforts to execute the ACTS action plan. The LHS Collaborative brings together diverse LHS stakeholders to enhance care transformation toward the Quintuple Aim and LHS function for clinical targets where improvements are urgently needed.

The Pain Management/Opioid Use LHS Learning Community (POLLC) is the first and most advanced LHS Collaborative community formed in follow-up to the ACTS initiative. POLLC includes multiple stakeholder groups (e.g., care team members, guideline developers, health IT suppliers, quality improvement organizations, standards experts, and patient advisors) who are working together to identify and close pain and opioid management care gaps in care delivery organizations (CDOs). Other targets being addressed by public and private CDOs supported by LHS Collaborative tools and approaches include hypertension control, sickle cell disease management, venous thromboembolism (VTE) prophylaxis, and chronic kidney disease.

This Lightning Talk will illustrate how CBK-related activities around the LHS cycle by the POLLC collaborative community and others could be integrated and enhanced to accelerate care transformation and LHS cycle function for this target. It will also outline how this approach could be scaled to address other high-priority conditions by ensuring that various target-focused LHS efforts contribute synergistically to building out a robust national/global knowledge ecosystem. In particular, by fostering a public-private partnership to drive progress toward the shared future vision, as called for in the ACTS action plan.

The authors plan to leverage this lightning talk and related explorations to transition the potential opportunities to leverage CBK to improve LHS cycle efficiency and effectiveness into real world advancements. And do this in a manner that cultivates synergies with other CBK-related activities to accelerate progress toward shared LHS goals.”

Philip Scott, University of Wales Trinity Saint David

[email protected]

Charlie McCay, Ramsey Systems

[email protected]

Shaun Rowark, NICE

[email protected]

Background: The National Institute for Health and Care Excellent (NICE), a non-departmental public body under the Department of Health and Social Care in England, has made a strategic commitment to producing clinical practice recommendations in a more modular and digital approach. In 2022–23, a first phase of work explored methods to convert narrative clinical recommendations to computable knowledge. The project involved NICE, MCBK-UK and a broad stakeholder group from industry, academia and healthcare providers. Two in-person ‘collaborathons’ were held, with fortnightly workstream calls in between, focused on NICE Guideline 28 (NG28), Type 2 diabetes in adults, specifically section 1.7 on drug treatment. The project adopted the World Health Organization (WHO) Digital Adaptation Kit (DAK) as a technology-agnostic method to model clinical practice recommendations. A NICE Computable Implementation Guide (NCIG) following the structure of the DAK was produced for NG28 section 1.7, including user scenarios, personae, processes and workflow, core data elements and decision-support logic. The first phase of work concluded in March 2023, with a recognition that further work was needed to demonstrate real-world utility, define indicators (outcomes such as prescribing compliance) and functional/non-functional requirements (such as data quality and usability) and to refine the documentation structure. Methods: The defined scope of the second phase started with real-world implementation. The project was loosely coupled with concurrent work on the diabetes information standard commissioned by NHS England from the Professional Record Standards Body (PRSB), an umbrella body formed from the medical Royal Colleges and professional bodies from across health and care. This enabled multi-disciplinary participation and drew upon PRSB's prior experience in development and implementation of clinical information standards. The selected implementation method was to define document templates (electronic forms) for primary care consultations by general practitioners (GPs) based on the core data elements and decision logic defined in the NCIG. GP document templates are a well-established and relatively straightforward way of standardizing records of care, as they enable pre-population from existing records, prompt for new data collection and can trigger links to practice workflows. Templates will also be defined for use in secondary care, and the effective sharing of information between primary and secondary care in support of the processes selected from NG28 and documented in the NCIG. Results: At the time of writing, GP template production is underway. By the time of the conference, we will report implementation experience of using the template with practices as well as outlining plans for outcome analysis and functional/non-functional requirements. We will also be able to report on initial work in how this is supporting NICE's wider portfolio of guidance products.

Simmi Sen, Stanford University

[email protected]

Eric Yeh, Stanford University

[email protected]

Sophia Wang, Stanford University

[email protected]

Background: Cataracts are caused by clouding of the natural lens that occurs with aging, leading to vision loss. Skilled completion of cataract surgery can result in almost immediate restoration of vision, but lengthy surgical time or poor instrument control may lead to prolonged or permanent vision loss. Cataract surgical trainees operate under the supervision of a preceptor who provides real-time feedback which is typically of a qualitative nature. Until now, there has been no way to track important characteristics of surgical performance related to tool usage or eye positioning. We previously developed deep learning methods to recognize the locations of key anatomical landmarks such as the pupil and its center, as well as surgical instruments and their tips from cataract surgical video, allowing for the calculation of AI-derived metrics related to performance, such as the total path length and area covered of individual tools, the velocity of tools, the centration of the eye, among others. The purpose of this study was to investigate whether these new AI-computed cataract surgery performance metrics correlate with surgeon seniority and skill as rated by human experts.

Methods: 28 resident and 29 attending routine cataract surgical videos were anonymously collected. For each video, 6 machine-generated metrics were generated by deep learning models: total path length, max velocity, area, phacoemulsification centration, eye fixation, and zoom level change. The former 3 metrics were individually calculated for limbus, pupil, and surgical instruments and the others were obtained at video level. Human raters independently rated the videos by Objective Structured Assessment of Cataract Surgical Skill (OSACSS), which had 20 subitems on a 5-point scale with larger indicating better performance. Statistical differences of machine- and human-rated scores between attending surgeons and trainees were tested by t tests, and the correlations between the two were examined by Pearson correlation coefficients.

Results: Phacoemulsification probe and irrigation/aspiration probe had significantly lower total path lengths, max velocities, and area metrics in attending videos. Attending surgeons exhibited better phacoemulsification centration and eye fixation. Most metrics correlated with average human-rated OSACSS scores, including tool-specific metrics and metrics related to microscope control (fixation: −.0.349; zoom level change: −0.322). Machine-generated metrics with corresponding OSACSS subitems also exhibited significant negative correlations (fixation: −0.65, phacoemulsification probe area metric: −0.67).

Conclusion: Automatically generated AI-metrics can be used to differentiate between attending and trainee surgeries and correlate with the human evaluation on surgical performance. These metrics can be automatically generated in a fast and scalable way in the post-surgical analysis, enabling surgical trainees to receive useful feedback in a timely manner during their training. In addition, the numerical values of these metrics can be logged and reviewed later to track the improvement in different facets of surgical skills. The model shows promise in building a fully automatic, objective surgical feedback system in ophthalmology training which will allow for the standardized and consistent analysis of surgical techniques.

Tongyue Shi, National Institute of Health Data Science, Peking University

[email protected]

Wentie Liu, National Institute of Health Data Science, Peking University

[email protected]

Shuai Jin, Department of Adult Care, School of Nursing, Capital Medical University

[email protected]

Jianguo Hao, National Institute of Health Data Science, Peking University, Institute of Medical Technology, Health Science Center of Peking University

[email protected]

Huiying Zhao, Peking University People's Hospital

[email protected]

Guilan Kong, National Institute of Health Data Science at Peking University, Advanced Institute of Information Technology, Peking University

[email protected]

Data-driven predictive analysis can help physicians in intensive care unit (ICU) to identify patients with high-risk mortality at an early stage, facilitate the provision of personalized interventions, and thus may lead to improved patient outcomes. Most existing studies employed traditional logistic regression or machine learning methods to develop ICU mortality prediction models, and the extreme lab test results during the first 8 h or 24 h of ICU admission were used as data inputs. In fact, there is a high degree of heterogeneity among ICU patients, and the trends of vital signs may be different among patients with different mortality risk. Is it possible to classify patients into different subgroups and then evaluate the ICU mortality risk accordingly?

Patient subgroup analysis aims to put patients with similar characteristics into one subgroup. It helps physicians gain a better understanding of disease patterns, facilitates the provision of personalized treatment plans, and eventually, it may help optimize critical care and improve patient outcomes.

The Medical Information Mart for Intensive Care (MIMIC-IV) database was used as data source. Time series data of five vital signs, including temperature, heart rate, mean arterial pressure (MAP), respiratory rate, and blood oxygen saturation (SpO2) were extracted for analysis.

An ensemble clustering model was developed for patient subgroup identification based on the time series vital signs data. It mainly comprised multivariate time-series feature extraction and clustering methods. Firstly, Time2Feat was employed as the method for time-series feature extraction. Then, different clustering algorithms including K-Means, K-Shape, and Agglomerative Hierarchical Clustering (AHC) were used as candidate clustering methods for subgroup analysis. In clustering, different distance metrics, including Euclidean distance, Dynamic Time Warping (DTW) and Soft-DTW distance were tried. Finally, based on the Davies-Bouldin Index (DBI) and Calinski-Harabaz Index (CHI), a most suitable clustering model was selected, and the optimal number of clusters (k) was determined using the Elbow Method.

Furthermore, based on the identified subgroups, an ICU mortality prediction model was to be developed. Patient personal information including age, gender, and medical history, together with the patient subgroup were used for model development. Ideally, if the patient grouping and mortality prediction models can be deployed in practice, when a patient has been in ICU for 8 hr, the patient would be assigned to a specific subgroup and the corresponding ICU mortality risk would be generated in time.

To conclude, an ensemble patient grouping model based on multivariate time-series vital signs data in the first 8 hr of ICU was developed in this study, and three distinct subgroups were identified. The development of an ICU mortality risk prediction model is still ongoing. Next step, we plan to implement the patient grouping and mortality prediction models in practice and evaluate real clinical effects.

Simon Šuster, University of Melbourne

[email protected]

Timothy Baldwin, Mohamed bin Zayed University of Artificial Intelligence

[email protected]

Karin Verspoor, RMIT University

[email protected]

Evidence-based medicine relies on expert analysis of scientific findings to address various clinical questions. In addition to locating, organizing, and representing this knowledge/evidence in structured form, a critical aspect is determining its quality. This includes identifying potential biases in methodologies, reporting, and publication of individual studies, as well as assessing the strength of larger bodies of evidence. These steps are crucial for synthesizing reliable evidence. However, their complexity and time-consuming nature hinder the accessibility of current medical knowledge.

In this talk, we will demonstrate how online collections of systematic reviews and published primary studies can be converted into machine readable datasets that capture key attributes of the evidence. These can then also be used for training and evaluating models for automated evidence assessment.

Relying on machine learning and natural language processing, these models can facilitate critical appraisal of medical evidence by not only predicting overall quality but also providing finer-grained justification for different quality criteria.

Furthermore, we will touch upon the topics of reliability through calibration of classifier confidence, selective classification (i.e., decreasing classifier's error rate by sacrificing coverage), and algorithmic fairness (i.e., disparity in different performance measures) in the developed models. Addressing these concerns is essential to ensure that the created quality classifiers are both accurate and unbiased. We believe that in practice, such systems are most likely to work well by working alongside rather than replacing medical experts, who usually construct systematic reviews manually. In effect, an automated system can be seen as an additional assessor against which the manual assessment can be compared and potentially revised. As automated approaches to quality assessment become increasingly accurate and reliable in the future, fewer human examiners may be needed, potentially leading to time and resource savings.

By integrating machine learning and natural language processing, we work toward the MCKB vision of structuring knowledge and ensuring that it reflects the best and most current evidence and science. This in turn can empower healthcare professionals to make more informed decisions for their patients.

Guy Tsafnat, PhD FAIDH, Founder and Chief Scientific Officer, Evidentli Pty Ltd

[email protected]

Kenny Hong, PhD, Head of Cloud Engineering. Evidentli Pty Ltd

[email protected]

Evidence Hub is a public, free website specifically designed to host open-source Computable Biomedical Knowledge Objects (CBKO). The site provides specific features to allow users define types of objects, provide applications to handle different objects, and form communities of interest around the objects.

Communities are provided with objected-centered open, moderated discussions with the aim of reaching consensus about implementation, correct use, and logic of the CBKO.

Evidence Hub has three public interfaces: a moderated discussion forum, a source code repository, and an application programming interface (API). All three interfaces are free for anyone to use. Uploading and editing source code, contributing to discussions, and using the API require free registration. Source code published on the site through the API must comply with a JSON format derived from the Knowledge Grid format: computable source code, a human-readable description, and a technical description of the execution environment for the object. In addition to these elements, the Evidence Hub format also has a meta-data section that includes identifiers, contributors, and version information that Evidence Hub needs to effectively manage permissions and versions of the object.

The source code repository has a dedicated “technical” page for each CBKO with code browsing, version history, a download source-code button, an option to create a “branch” copy of the object, and links to other technical resources such as technical documentation. Contributions to the source code can be uploaded, but inclusion in the object is subject to approval by the object's owner.

Each CBKO also has a “non-technical” page. The main features of this page are a human-readable description of the object and a discussion forum. The human readable description of the object is included in the object structure and is displayed here. The discussion forum is multi-threaded and moderated by the object's owner.

Both pages have several common elements. Users that were logged in through a registered application (see API description below), will also see a one-click import button on pages of CBKOs corresponding to the application. Social features such as sharing, posting, and watching CBKO's are also found on both pages.

The API provides similar functionality to the technical interface as well as endpoints to define new object types, and to register an application's capability to edit and/or execute an object of a particular type. The option to use the application will only be available to users already logged in through the application.

Knowledge hosted on the Evidence Hub remains with its owner. CBKOs must have a GPL 2.0 or compatible license. Evidence Hub is free of advertising and user information is only ever used to improve the Evidence Hub itself.

Minerva Viguera Moreno, Universidad Nacional de Educacion a Distancia (U.N.E.D)

[email protected]

María Eugenia Marzo-Sola, Hospital San Pedro, Spain

[email protected]

Fernando Martin-Sanchez, Hospital Universitario La Paz

[email protected]

Multiple Sclerosis is a neurodegenerative disease which shows different phenotypes making difficult for clinicians to make short-term decisions related with treatment and prognosis. Diagnosis is usually retrospective. Learning Healthcare Systems (LHS) can support clinical practice as they are devised as constantly improving modules. LHS can identify insights which allow evidence-based clinical decisions and more accurate prognosis.

We are developing a LHS with the aim of reducing uncertainty. We are using REDCap™ to collect patients' data, both from Clinical Reported Outcomes (CRO) and from Patients Reported Outcomes (PRO). Once analyzed, this data will serve as a foundation to our LHS. We conducted bibliographical research to select those CRO and PRO collected in clinical practice or identified as possible risk factors. We designed a data collection and management protocol based on using REDCap™ which ensures anonymity and allows longitudinal and semi-automated capture of health data. We are following a cohort of 300 patients for 18 months. At the moment, we have included 198 patients and received 141 complete responses and 7 partial responses regarding PROs baseline (70%). We are collecting PROs every 3 months; until the date, 59 patients have reach end this first endpoint: 32 answered and 27 have been requested by mail (54%). This data will be used to develop a LHS, able to accurate prognosis as well as to automatically include new data and improve its algorithm.

Although our results are preliminary, we are finding promising response rates from patients in the first round. On the other hand, rates from the second round are low and could compromise the longitudinal information needed for the LHS development.

Hannah Wood, NHS England

[email protected]

Large Language Models (LLMs) excel at language-based tasks and can save considerable time if used appropriately and with skill. In this session, I will demonstrate that with careful prompting, LLMs like Bard and GPT-4 can generate robust and comprehensive Boolean search strategies for use in Google and advanced search databases.

With urgent time pressures in healthcare becoming more prominent than ever before, and an ever-increasing body of healthcare knowledge, LLMs provide an opportunity to save time without necessarily compromising quality. With newly accessible technologies, new search skills are required to responsibly search for healthcare topics.

While LLM responses are generally regarded as untrustworthy due to concerns around hallucination risks, bias and misinformation, language models are ideally placed to generate efficient and extensive Boolean search strategies for use in search databases. However, to achieve robust search strategies, knowledge of prompting tools for appropriate responses is required.

There are certain elements to avoid or be cognisant of when prompting LLMs to generate or develop search strategies: avoiding the generation of inaccurate Medical Subject Headings and search operators incompatible with search databases and ensuring that the search strategy itself is factually accurate.

Language models excel at tasks involving language; they can generate various synonyms and expand upon Boolean search blocks when prompted. These search blocks can be effectively used in Google and other search databases, improving search results, and locating relevant literature. They can also be useful for developing existing strategies.

With over 6 months of extensive testing, I conclude that language models have a place in the growing toolset of healthcare Knowledge and Library specialists, and those who frequently search the healthcare literature. LLMs can generate quality search blocks when prompted by a skilled searcher with an understanding of healthcare topics and experience with using Boolean logic in search databases; ultimately saving time and improving the quality of the searches.

Jeremy C Wyatt, University of Southampton

[email protected]

Philip Scott, Programme Director, Institute of Management and Health, University of Wales Trinity Saint David

[email protected]

Matthew South, Director and Chief Technology Officer, OpenClinical CIC and Senior Software Architect at University of Birmingham

[email protected]

Mark Thomas, Associate Professor of Cardiology, University of Birmingham

[email protected]

Caroline Jones, Associate Professor, Hillary Rodham Clinton School of Law, Swansea University

[email protected]

Our aim in this project (sponsored by the UK MCBK chapter and the British Computer Society) was to understand which knowledge objects in a computable biomedical knowledge library are likely to be subject to regulation as a medical device in the UK. To achieve this understanding, a briefing paper was circulated to a multi-disciplinary group of 25 people including regulators, lawyers, software engineers, digital health academics, librarians, and others with insights into knowledge management and device regulation. A one-day workshop was then convened to discuss some key questions relating to our aim. Following wide ranging discussion by participants and further assessment of relevant regulations, a discussion paper was drafted by lead authors and circulated to other authors for their comments.

This contribution will report on how UK medical device regulators are likely to treat the different kinds of knowledge objects that may be stored in computable biomedical knowledge libraries. While our focus is on the likely approach taken by UK regulators, our UK regulators have contributed to the work of the International Medical Device Regulators Forum so our analysis will also be relevant to the approaches taken by regulators elsewhere.

We outline the UK criteria for medical devices and the responsibility of device manufacturers, then examine the regulatory implications for knowledge objects that correspond with each of the four knowledge levels described by Boxwala in 2011. We also propose an additional knowledge level for tagged fragments of guidelines etc. that we call level 2b, which is positioned between level 2 and level 3.

Our main conclusion is that if a knowledge object is directly executable and is described or marketed as being intended for a medical purpose to provide decision support, it will be in scope of UK regulation as “software as a medical device”. Conversely, if the knowledge object is not directly executable (e.g., it consists of an algorithm, a ruleset, an order set, pseudocode or some other knowledge representation) or the developers make no claim that the object can be used directly for a medical purpose (e.g., it is presented as pluripotential, so could be used to support medical research or education), it is not likely to be subject to UK regulation.

We expect similar reasoning to be applied in other countries with similar regulatory principles.

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2023 MCBK 全球会议-闪电讲座摘要

本研究旨在开发基于机器学习的工具，该工具可根据脓毒症发生最初 3 小时内收集的临床数据预测重症监护室内脓毒症患者的住院死亡率风险。液体疗法是一种临床治疗方法，主要通过补充或限制特定液体来维持液体平衡。作为一种危及生命的疾病，早期复苏量对脓毒症患者非常重要，它影响着患者的预后和治疗效果。遗憾的是，现有的大多数脓毒症死亡率预测模型都没有将早期复苏量纳入分析范围。在临床实践中，脓毒症患者应尽早进行液体复苏。2016 年 "脓毒症生存运动 "指南建议，在脓毒症患者复苏的最初 3 小时内，应给予至少 30 mL/kg 的静脉注射晶体液。从指南中可以看出，脓毒症确诊后的最初 3 小时被视为早期复苏的关键 "黄金时间"。因此，本研究将早期复苏干预纳入了死亡率风险预测模型。数据来源是重症监护医学信息市场-IV（MIMIC-IV）数据库，该数据库包含 2008 年至 2019 年期间贝斯以色列女执事医疗中心重症监护室收治的 4 万多名重症监护室患者的记录。从 MIMIC-IV 中提取的脓毒症患者数据形成了一个庞大的研究群体，其中包含的临床信息包括人口统计学、实验室检查、临床评估和医疗治疗。在分析方法方面，本研究采用了几种具有良好可解释性的机器学习方法，包括随机森林（RF）和极端梯度提升（XGBoost），以及多元逻辑回归，这些方法在医学领域也表现出了令人满意的预测能力。最后，将两种基于机器学习的模型的预测性能与传统的逻辑回归进行了比较，并选择了性能最佳的预测模型作为临床推荐。本研究开发的预测工具将有助于早期识别院内死亡风险较高的败血症患者。希望它能帮助重症监护室的医生提供及时、最佳的干预措施，从而有助于改善重症监护室患者的预后，降低院内死亡率。不断加强手术培训是确保高手术标准和保持良好手术视觉效果的关键。传统上，手术培训和反馈几乎完全基于手术室中由教员主导的实时反馈。机器学习（特别是深度学习模型）的出现有可能通过分析常规拍摄的手术视频，对手术表现进行更精细、更客观的分析，从而增强手术反馈。在之前的工作中，我们开发了深度学习模型，可以识别白内障手术视频中的关键手术地标和正在进行的手术步骤，从而提供了一种量化评估手术技能的新方法。PhacoTrainer 平台是一个基于网络的应用程序，用户可以上传白内障手术视频，并获得对其白内障手术表现的见解。该平台针对上传的视频部署了一个深度学习模型，即混合卷积神经网络和循环神经网络，以检测哪些手术包含特殊的手术技术或并发症。模型输出还能计算出手术每个步骤所花费的时间，然后将其显示在仪表板上，直观显示外科医生积累更多经验后每个步骤手术时间的变化。每个手术视频的时间轴也会自动注释，逐帧确定正在进行的手术步骤，以便外科医生更好地浏览手术视频。因此，PhacoTrainer 根据这些模型提供的反馈意见为外科医生提供了有洞察力的指标，以监控他们在多个维度上的手术表现，找出可能需要改进的地方。 PhacoTrainer 平台预示着白内障手术培训领域的重大进步，它将非结构化的白内障手术视频转化为可计算的洞察力。通过利用深度学习对手术视频进行客观分析，它为外科医生提供了自我评估技能、跟踪改进情况、记录手术元数据并最终提高手术效果的工具。PhacoTrainer 还能为所有学员提供高质量的反馈，不受地域或机构限制。PhacoTrainer 能够积累大量有关白内障手术的元数据，它还有望促进未来有关白内障手术的研究，促进对手术技术随时间推移和白内障手术培训的更细致入微的了解。"犹他大学医学博士 Alan H. Morris[email protected]对于可复制的决策支持小组来说，临床决策是以知识、专长和权威为基础的，临床医生根据希波克拉底临床决策模式批准几乎所有的干预措施。这是提供 "所有正确的医疗服务，但只有正确的医疗服务 "的起点，但这一质量目标尚未实现，因为在没有辅助的情况下，临床医生仅根据自己的培训、专业知识和经验做出决策时，会受到人类认知局限性和偏见的影响。强大的决策支持工具可以减少临床医生决策和行动中不必要的偏差，从而改善医疗服务。目前的电子病历（EHR）侧重于结果审查、记录和核算。电子病历既笨拙又耗时，还会造成临床医生的压力和职业倦怠。决策支持工具可以减轻临床医生的负担，并使临床医生的决策和行动具有可复制性，从而实现对患者的个性化护理。然而，目前大多数临床决策支持工具/辅助工具缺乏细节，既不能减轻临床医生的负担，也不能让临床医生采取可复制的行动。临床医生必须提供主观解释和缺失的逻辑，从而引入了个人偏见和无意识、无理由的循证实践差异。当不同的临床医生在相同的患者信息和背景下做出相同的决定和行动时，就会出现可复制性。基于可靠临床结果证据的治疗决策支持工具的一个子集是计算机协议（eActions），包括闭环系统，可导致临床医生采取可复制的行动。在先进的现代医疗保健服务环境中，eActions 克服了负担过重的临床医生的认知局限性。eActions 包括以证据、经验、电子病历数据和患者个体状况为依据的良好日常决策。eActions 可以减少临床医生不必要的差异，提高临床护理和研究质量，减少电子病历噪音，并可实现学习型医疗保健系统。循证指南只能解决一小部分临床护理问题。医疗服务不足的地区很少能实时获得最先进的循证指南，也往往无法实施先进的指南。这些地区的医疗服务提供者往往没有足够的培训或时间来实施先进的指南。要广泛使用电子行动，就必须克服当前的医疗保健技术和文化障碍，并安装临床证据/数据整理系统，以便在真正的学习型医疗保健系统中，通过在常规医疗保健服务过程中开展的比较有效性临床研究，产生新的或修改过的循证指南。佛蒙特大学医学中心的 Katelin Morrissette[email protected]医疗决策的许多重要组成部分，如诊断的确定性、考虑但避免的干预措施或患者在管理决策中的投入，都很难通过医疗记录中的现有数据元素来衡量。我们介绍了一种建立自定义数据元素的方法，以反映医疗管理的这些组成部分，并描述了实施过程。医疗管理的新创新可能无法在电子病历的传统元素中体现，因此也将依赖于这些定制的数据元素。例如，在重症监护医学中，病人进入重症监护室（ICU）之前的护理阶段可被视为重症监护室周边阶段（peri-ICU）。这一阶段的干预措施可以避免患者进入重症监护室，或确定初步诊断和管理。主刀医生的乳化中心定位和眼球固定效果更好。大多数指标与人类评定的 OSACSS 平均分相关，包括特定工具指标和与显微镜控制相关的指标（固定：-0.349；变焦水平变化：-0.322）。机器生成的指标与相应的 OSACSS 子项目也表现出显著的负相关（固定：-0.65；超声乳化探头面积指标：-0.67）：自动生成的人工智能指标可用于区分主治医生和实习医生的手术，并与人类对手术表现的评价相关联。这些指标可以在手术后分析中以快速、可扩展的方式自动生成，使外科学员在培训期间及时获得有用的反馈。此外，这些指标的数值可以被记录下来，并在以后进行审查，以跟踪手术技能不同方面的改进情况。该模型有望在眼科培训中建立一个全自动、客观的手术反馈系统，从而对手术技术进行标准化和一致的分析。史彤悦，北京大学国家健康数据科学研究院[email protected]刘文铁，北京大学国家健康数据科学研究院[email protected]金帅，首都医科大学护理学院成人护理系[email protected]郝建国，北京大学国家健康数据科学研究院、北京大学医学部医学技术研究所、北京大学健康科学中心[email protected]赵慧英、北京大学人民医院[email protected]孔桂兰，北京大学健康数据科学国家研究院、北京大学信息技术高等研究院[email protected]数据驱动的预测分析可以帮助重症监护病房（ICU）的医生早期识别高危死亡患者，便于提供个性化干预，从而改善患者预后。现有研究大多采用传统的逻辑回归或机器学习方法来开发 ICU 死亡率预测模型，并将入院 8 小时或 24 小时内的极端实验室检测结果作为数据输入。事实上，ICU 患者之间存在高度异质性，不同死亡风险的患者生命体征的变化趋势也可能不同。是否有可能将患者分为不同的亚组，然后据此评估 ICU 死亡率风险？它能帮助医生更好地了解疾病模式，便于提供个性化的治疗方案，最终可能有助于优化重症监护和改善患者预后。该研究以重症监护医学信息市场（MIMIC-IV）数据库为数据源，提取了包括体温、心率、平均动脉压（MAP）、呼吸频率和血氧饱和度（SpO2）在内的五项生命体征的时间序列数据进行分析。该模型主要包括多元时间序列特征提取和聚类方法。首先，采用 Time2Feat 作为时间序列特征提取方法。然后，采用不同的聚类算法，包括 K-Means、K-Shape 和聚合分层聚类（AHC），作为亚组分析的候选聚类方法。在聚类过程中，尝试了不同的距离度量，包括欧氏距离、动态时间扭曲（DTW）和软-DTW 距离。最后，根据戴维斯-博尔丁指数（DBI）和卡林斯基-哈拉巴兹指数（CHI），选择了一个最合适的聚类模型，并使用肘法确定了最佳聚类数目（k）。患者的个人信息包括年龄、性别、病史以及患者亚群，这些信息都被用于模型的开发。如果患者分组和死亡率预测模型能在实践中应用，那么当患者在重症监护室住院 8 小时后，患者将被分配到一个特定的亚组，并及时生成相应的重症监护室死亡风险。最后，本研究基于重症监护室前 8 小时的多变量时间序列生命体征数据建立了一个患者分组集合模型，并确定了三个不同的亚组。ICU 死亡率风险预测模型的开发工作仍在进行中。下一步，我们计划在实践中实施患者分组和死亡率预测模型，并评估实际临床效果。 Jeremy C Wyatt，南安普顿大学[email protected]Philip Scott，威尔士三一圣大卫大学管理与健康研究所项目主任[email protected]Matthew South，OpenClinical CIC 董事兼首席技术官，伯明翰大学高级软件架构师[email protected]Mark Thomas，心脏病学副教授、伯明翰大学[email protected]Caroline Jones，斯旺西大学希拉里-罗德姆-克林顿法学院副教授[email protected]我们的项目（由英国 MCBK 分会和英国计算机学会赞助）旨在了解可计算生物医学知识库中的哪些知识对象有可能在英国作为医疗设备受到监管。为了实现这一认识，我们向一个由 25 人组成的多学科小组分发了一份简报，其中包括监管人员、律师、软件工程师、数字健康学者、图书馆员以及其他对知识管理和设备监管有深刻见解的人员。随后召开了为期一天的研讨会，讨论与我们的目标相关的一些关键问题。经过与会者的广泛讨论和对相关法规的进一步评估，主要作者起草了一份讨论文件，并分发给其他作者征求意见。本文将报告英国医疗器械监管机构可能会如何对待可计算生物医学知识库中存储的不同类型的知识对象。虽然我们的重点是英国监管机构可能采取的方法，但英国监管机构为国际医疗器械监管机构论坛的工作做出了贡献，因此我们的分析也将与其他地方监管机构采取的方法相关。我们概述了英国的医疗器械标准和器械制造商的责任，然后研究了与 Boxwala 在 2011 年描述的四个知识水平相对应的知识对象的监管影响。我们的主要结论是，如果一个知识对象是可直接执行的，并且被描述或推销为用于医疗目的以提供决策支持，那么它将作为 "作为医疗设备的软件 "而属于英国的监管范围。反之，如果知识对象不可直接执行（例如，它由算法、规则集、顺序集、伪代码或其他知识表示法组成），或者开发者没有声称该对象可直接用于医疗目的（例如，它被描述为多能性，因此可用于支持医学研究或教育），那么它就不可能受到英国的监管。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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