Methods of Information in Medicine最新文献

Background: MetaMap is a valuable tool for processing biomedical texts to identify concepts. Although MetaMap is highly configurative, configuration decisions are not straightforward.

Objective: To develop a systematic, data-driven methodology for configuring MetaMap for optimal performance.

Methods: MetaMap, the word2vec model, and the phrase model were used to build a pipeline. For unsupervised training, the phrase and word2vec models used abstracts related to clinical decision support as input. During testing, MetaMap was configured with the default option, one behavior option, and two behavior options. For each configuration, cosine and soft cosine similarity scores between identified entities and gold-standard terms were computed for 40 annotated abstracts (422 sentences). The similarity scores were used to calculate and compare the overall percentages of exact matches, similar matches, and missing gold-standard terms among the abstracts for each configuration. The results were manually spot-checked. The precision, recall, and F-measure (β =1) were calculated.

Results: The percentages of exact matches and missing gold-standard terms were 0.6-0.79 and 0.09-0.3 for one behavior option, and 0.56-0.8 and 0.09-0.3 for two behavior options, respectively. The percentages of exact matches and missing terms for soft cosine similarity scores exceeded those for cosine similarity scores. The average precision, recall, and F-measure were 0.59, 0.82, and 0.68 for exact matches, and 1.00, 0.53, and 0.69 for missing terms, respectively.

Conclusion: We demonstrated a systematic approach that provides objective and accurate evidence guiding MetaMap configurations for optimizing performance. Combining objective evidence and the current practice of using principles, experience, and intuitions outperforms a single strategy in MetaMap configurations. Our methodology, reference codes, measurements, results, and workflow are valuable references for optimizing and configuring MetaMap.

Background: While there is a rapid increase in digital health initiatives focusing on the processing of personal data for strengthening the delivery of reproductive, maternal, newborn, and child health (RMNCH) services in fragile settings, these are often unaccompanied at both the policy and operational levels with adequate legal and regulatory frameworks.

Objective: The main aim was to understand the maturity level of digital personal data initiatives for RMNCH services within fragile contexts. This aim was performed by choosing digital health initiatives from each country (two in Jordan and three in Palestine) based on RMNCH.

Methods: A qualitative study design was adopted. We developed a digital maturity assessment tool assessing two maturity levels: the information and communications technology digital infrastructure, and data governance and interoperability in place for the five selected RMNCH initiatives in Jordan and Palestine.

Results: Overall, the digital infrastructure and technological readiness components are more advanced and show higher maturity levels compared with data governance and interoperability components in Jordan and Palestine. In Jordan, the overall Jordan stillbirths and neonatal deaths surveillance initiative maturity indicators are somehow less advanced than those of the Electronic Maternal and Child Health Handbook-Jordan (EMCH-J) application. In Palestine, the Electronic Maternal and Child Health-registry initiative maturity indicators are more advanced than both Avicenna and EMCH-Palestine initiatives.

Conclusion: The findings highlighted several challenges and opportunities around the application and implementation of selected digital health initiatives in the provision of RMNCH in Jordan and Palestine. Our findings shed lights on the maturity level of these initiatives within fragile contexts. The maturity level of the five RMNCH initiatives in both countries is inadequate and requires further advancement before they can be scaled up and scaled out. Taking the World Health Organization recommendations into account when developing, implementing, and scaling digital health initiatives in low- and middle-income countries can result in successful and sustainable initiatives, thus meeting health needs and improving the quality of health care received by individuals especially those living in fragile contexts.

Background: During the COVID-19 pandemic, several methodologies were designed for obtaining electronic health record (EHR)-derived datasets for research. These processes are often based on black boxes, on which clinical researchers are unaware of how the data were recorded, extracted, and transformed. In order to solve this, it is essential that extract, transform, and load (ETL) processes are based on transparent, homogeneous, and formal methodologies, making them understandable, reproducible, and auditable.

Objectives: This study aims to design and implement a methodology, according with FAIR Principles, for building ETL processes (focused on data extraction, selection, and transformation) for EHR reuse in a transparent and flexible manner, applicable to any clinical condition and health care organization.

Methods: The proposed methodology comprises four stages: (1) analysis of secondary use models and identification of data operations, based on internationally used clinical repositories, case report forms, and aggregated datasets; (2) modeling and formalization of data operations, through the paradigm of the Detailed Clinical Models; (3) agnostic development of data operations, selecting SQL and R as programming languages; and (4) automation of the ETL instantiation, building a formal configuration file with XML.

Results: First, four international projects were analyzed to identify 17 operations, necessary to obtain datasets according to the specifications of these projects from the EHR. With this, each of the data operations was formalized, using the ISO 13606 reference model, specifying the valid data types as arguments, inputs and outputs, and their cardinality. Then, an agnostic catalog of data was developed through data-oriented programming languages previously selected. Finally, an automated ETL instantiation process was built from an ETL configuration file formally defined.

Conclusions: This study has provided a transparent and flexible solution to the difficulty of making the processes for obtaining EHR-derived data for secondary use understandable, auditable, and reproducible. Moreover, the abstraction carried out in this study means that any previous EHR reuse methodology can incorporate these results into them.

Background: Clinical trials, epidemiological studies, clinical registries, and other prospective research projects, together with patient care services, are main sources of data in the medical research domain. They serve often as a basis for secondary research in evidence-based medicine, prediction models for disease, and its progression. This data are often neither sufficiently described nor accessible. Related models are often not accessible as a functional program tool for interested users from the health care and biomedical domains.

Objective: The interdisciplinary project Leipzig Health Atlas (LHA) was developed to close this gap. LHA is an online platform that serves as a sustainable archive providing medical data, metadata, models, and novel phenotypes from clinical trials, epidemiological studies, and other medical research projects.

Methods: Data, models, and phenotypes are described by semantically rich metadata. The platform prefers to share data and models presented in original publications but is also open for nonpublished data. LHA provides and associates unique permanent identifiers for each dataset and model. Hence, the platform can be used to share prepared, quality-assured datasets and models while they are referenced in publications. All managed data, models, and phenotypes in LHA follow the FAIR principles, with public availability or restricted access for specific user groups.

Results: The LHA platform is in productive mode (https://www.health-atlas.de/). It is already used by a variety of clinical trial and research groups and is becoming increasingly popular also in the biomedical community. LHA is an integral part of the forthcoming initiative building a national research data infrastructure for health in Germany.

Background: One Digital Health (ODH) aims to propose a framework that merges One Health's and Digital Health's specific features into an innovative landscape. FAIR (Findable, Accessible, Interoperable, and Reusable) principles consider applications and computational agents (or, in other terms, data, metadata, and infrastructures) as stakeholders with the capacity to find, access, interoperate, and reuse data with none or minimal human intervention.

Objectives: This paper aims to elicit how the ODH framework is compliant with FAIR principles and metrics, providing some thinking guide to investigate and define whether adapted metrics need to be figured out for an effective ODH Intervention setup.

Methods: An integrative analysis of the literature was conducted to extract instances of the need-or of the eventual already existing deployment-of FAIR principles, for each of the three layers (keys, perspectives and dimensions) of the ODH framework. The scope was to assess the extent of scatteredness in pursuing the many facets of FAIRness, descending from the lack of a unifying and balanced framework.

Results: A first attempt to interpret the different technological components existing in the different layers of the ODH framework, in the light of the FAIR principles, was conducted. Although the mature and working examples of workflows for data FAIRification processes currently retrievable in the literature provided a robust ground to work on, a nonsuitable capacity to fully assess FAIR aspects for highly interconnected scenarios, which the ODH-based ones are, has emerged. Rooms for improvement are anyway possible to timely deal with all the underlying features of topics like the delivery of health care in a syndemic scenario, the digital transformation of human and animal health data, or the digital nature conservation through digital technology-based intervention.

Conclusions: ODH pillars account for the availability (findability, accessibility) of human, animal, and environmental data allowing a unified understanding of complex interactions (interoperability) over time (reusability). A vision of integration between these two worlds, under the vest of ODH Interventions featuring FAIRness characteristics, toward the development of a systemic lookup of health and ecology in a digitalized way, is therefore auspicable.

Background: Privacy management is a key issue when dealing with storage and distribution of health information. However, FAIR (Findability, Accessibility, Interoperability, and Reusability) principles when sharing information are in increasing demand in several organizations, especially for information generated in public-funded research projects.

Objectives: The two main objectives of the presented work are the definition of a secure and interoperable modular architecture to manage different kinds of medical content (xIPAMS [x, for Any kind of content, Information Protection And Management System] and HIPAMS [Health Information Protection And Management System]), and the application of FAIR principles to that architecture in such a way that privacy and security are compatible with FAIR.

Methods: We propose the concept of xIPAMS as a modular architecture, following standards for interoperability, which defines mechanisms for privacy, protection, storage, search, and access to health-related information.

Results: xIPAMS provides FAIR principles and preserves patient's privacy. For each module, we identify how FAIR principles apply.

Conclusions: We have analyzed how xIPAMS, and in particular HIPAMS (Health content), support the FAIR principles focusing on security and privacy. We have identified the FAIR principles supported by the different xIPAMS modules, concluding that the four principles are supported. Our analysis has also considered a possible implementation based on the concept of DACS (Document Access and Communication System), a system storing medical documents in a private and secure way. In addition, we have analyzed security aspects of the FAIRification process and how they are provided by xIPAMS modules.

Background: Automated clinical decision support for risk assessment is a powerful tool in combating cardiovascular disease (CVD), enabling targeted early intervention that could avoid issues of overtreatment or undertreatment. However, current CVD risk prediction models use observations at baseline without explicitly representing patient history as a time series.

Objective: The aim of this study is to examine whether by explicitly modelling the temporal dimension of patient history event prediction may be improved.

Methods: This study investigates methods for multivariate sequential modelling with a particular emphasis on long short-term memory (LSTM) recurrent neural networks. Data from a CVD decision support tool is linked to routinely collected national datasets including pharmaceutical dispensing, hospitalization, laboratory test results, and deaths. The study uses a 2-year observation and a 5-year prediction window. Selected methods are applied to the linked dataset. The experiments performed focus on CVD event prediction. CVD death or hospitalization in a 5-year interval was predicted for patients with history of lipid-lowering therapy.

Results: The results of the experiments showed temporal models are valuable for CVD event prediction over a 5-year interval. This is especially the case for LSTM, which produced the best predictive performance among all models compared achieving AUROC of 0.801 and average precision of 0.425. The non-temporal model comparator ridge classifier (RC) trained using all quarterly data or by aggregating quarterly data (averaging time-varying features) was highly competitive achieving AUROC of 0.799 and average precision of 0.420 and AUROC of 0.800 and average precision of 0.421, respectively.

Conclusion: This study provides evidence that the use of deep temporal models particularly LSTM in clinical decision support for chronic disease would be advantageous with LSTM significantly improving on commonly used regression models such as logistic regression and Cox proportional hazards on the task of CVD event prediction.

Background: In recent years, researchers have used many computerized interventions to reduce medical errors, the third cause of death in developed countries. One of such interventions is using differential diagnosis generators in primary care, where physicians may encounter initial symptoms without any diagnostic presuppositions. These systems generate multiple diagnoses, ranked by their likelihood. As such, these reports' accuracy can be determined by the location of the correct diagnosis in the list.

Objective: This study aimed to design and evaluate a novel practical web-based differential diagnosis generator solution in primary care.

Methods: In this research, a new online clinical decision support system, called DxGenerator, was designed to improve diagnostic accuracy; to this end, an attempt was made to converge a semantic database with the unified medical language system (UMLS) knowledge base, using MetaMap tool and natural language processing. In this regard, 120 diseases of gastrointestinal organs causing abdominal pain were modeled into the database. After designing an inference engine and a pseudo-free-text interactive interface, 172 patient vignettes were inputted into DxGenerator and ISABEL, the most accurate similar system. The Wilcoxon signed ranked test was used to compare the position of correct diagnoses in DxGenerator and ISABEL. The α level was defined as 0.05.

Results: On a total of 172 vignettes, the mean and standard deviation of correct diagnosis positions improved from 4.2 ± 5.3 in ISABEL to 3.2 ± 3.9 in DxGenerator. This improvement was significant in the subgroup of uncommon diseases (p-value < 0.05).

Conclusion: Using UMLS knowledge base and MetaMap Tools can improve the accuracy of diagnostic systems in which terms are entered in a free text manner. Applying these new methods will help the medical community accept medical diagnostic systems better.

Objective: The aim of the study is to identify the important clinical variables found in both pregnant and non-pregnant women who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, using an artificial intelligence (AI) platform.

Methods: This was a retrospective cohort study of all women between the ages of 18 to 45, who were admitted to Maimonides Medical Center between March 10, 2020 and December 20, 2021. Patients were included if they had nasopharyngeal PCR swab positive for SARS-CoV-2. Safe People Artificial Intelligence (SPAI) platform, developed by Gynisus, Inc., was used to identify key clinical variables predicting a positive test in pregnant and non-pregnant women. A list of mathematically important clinical variables was generated for both non-pregnant and pregnant women.

Results: Positive results were obtained in 1,935 non-pregnant women and 1,909 non-pregnant women tested negative for SARS-CoV-2 infection. Among pregnant women, 280 tested positive, and 1,000 tested negative. The most important clinical variable to predict a positive swab result in non-pregnant women was age, while elevated D-dimer levels and presence of an abnormal fetal heart rate pattern were the most important clinical variable in pregnant women to predict a positive test.

Conclusion: In an attempt to better understand the natural history of the SARS-CoV-2 infection we present a side-by-side analysis of clinical variables found in pregnant and non-pregnant women who tested positive for COVID-19. These clinical variables can help stratify and highlight those at risk for SARS-CoV-2 infection and shed light on the individual patient risk for testing positive.

Background: Allergy is a hypersensitive reaction that occurs when the allergen reacts with the immune system. The prevalence and severity of the allergies are uprising in South Asian countries. Allergy often occurs in combinations which becomes difficult for physicians to diagnose.

Objectives: This work aims to develop a decision-making model which aids physicians in diagnosing allergy comorbidities. The model intends to not only provide rational decisions, but also explainable knowledge about all alternatives.

Methods: The allergy data gathered from real-time sources contain a smaller number of samples for comorbidities. Decision-making model applies three sampling strategies, namely, ideal, single, and complete, to balance the data. Bayes theorem-based probabilistic approaches are used to extract knowledge from the balanced data. Preference weights for attributes with respect to alternatives are gathered from a group of domain-experts affiliated to different allergy testing centers. The weights are combined with objective knowledge to assign confidence values to alternatives. The system provides these values along with explanations to aid decision-makers in choosing an optimal decision.

Results: Metrics of explainability and user satisfaction are used to evaluate the effectiveness of the system in real-time diagnosis. Fleiss' Kappa statistic is 0.48, and hence the diagnosis of experts is said to be in moderate agreement. The decision-making model provides a maximum of 10 suitable and relevant pieces of evidence to explain a decision alternative. Clinicians have improved their diagnostic performance by 3% after using CDSS (77.93%) with a decrease in 20% of time taken.

Conclusion: The performance of less-experienced clinicians has improved with the support of an explainable decision-making model. The code for the framework with all intermediate results is available at https://github.com/kavya6697/Allergy-PT.git.