International Journal of Medical Informatics最新文献

{"title":"Large language models can support generation of standardized discharge summaries – A retrospective study utilizing ChatGPT-4 and electronic health records","authors":"","doi":"10.1016/j.ijmedinf.2024.105654","DOIUrl":"10.1016/j.ijmedinf.2024.105654","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate whether psychiatric discharge summaries (DS) generated with ChatGPT-4 from electronic health records (EHR) can match the quality of DS written by psychiatric residents.</div></div><div><h3>Methods</h3><div>At a psychiatric primary care hospital, we compared 20 inpatient DS, written by residents, to those written with ChatGPT-4 from pseudonymized residents’ notes of the patients’ EHRs and a standardized prompt. 8 blinded psychiatry specialists rated both versions on a custom Likert scale from 1 to 5 across 15 quality subcategories. The primary outcome was the overall rating difference between the two groups. The secondary outcomes were the rating differences at the level of individual question, case, and rater.</div></div><div><h3>Results</h3><div>Human-written DS were rated significantly higher than AI (mean ratings: human 3.78, AI 3.12, p &lt; 0.05). They surpassed AI significantly in 12/15 questions and 16/20 cases and were favored significantly by 7/8 raters. For “low expected correction effort”, human DS were rated as 67 % favorable, 19 % neutral, and 14 % unfavorable, whereas AI-DS were rated as 22 % favorable, 33 % neutral, and 45 % unfavorable. Hallucinations were present in 40 % of AI-DS, with 37.5 % deemed highly clinically relevant. Minor content mistakes were found in 30 % of AI and 10 % of human DS. Raters correctly identified AI-DS with 81 % sensitivity and 75 % specificity.</div></div><div><h3>Discussion</h3><div>Overall, AI-DS did not match the quality of resident-written DS but performed similarly in 20% of cases and were rated as favorable for “low expected correction effort” in 22% of cases. AI-DS lacked most in content specificity, ability to distill key case information, and coherence but performed adequately in conciseness, adherence to formalities, relevance of included content, and form.</div></div><div><h3>Conclusion</h3><div>LLM-written DS show potential as templates for physicians to finalize, potentially saving time in the future.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review and proposed framework for sustainable learning healthcare systems","authors":"","doi":"10.1016/j.ijmedinf.2024.105652","DOIUrl":"10.1016/j.ijmedinf.2024.105652","url":null,"abstract":"<div><h3>Background</h3><div>The healthcare sector is a complex domain that faces challenges in effectively learning from practices and outcome data. The Learning Health System (LHS) has emerged as a potential framework to improve healthcare by promoting continuous learning. However, its adoption remains limited, often involving only a single clinical department or a part of the LHS cycle. There is a need to gain a better understanding of implementing LHS on a larger scale.</div></div><div><h3>Aim</h3><div>To identify complete implementations of the LHS for providing recommendations into their implementation strategies, success factors, barriers, and outcomes.</div></div><div><h3>Methods</h3><div>A systematic review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines using PubMed and Scopus databases. Data from the included papers were thematically categorized into four primary areas: (1) Scale of LHS Implementation; (2) Implementation strategies and the factors that facilitated the implementation of LHS; (3) LHS outcomes; and (4) Barriers /challenges related to the LHS implementation.</div></div><div><h3>Results</h3><div>We identified 1,279 papers, of which 37 were included in the final analysis. Barriers to implementing LHS included interoperability, data integration, electronic health records (EHRs) challenges, organizational culture, leadership, and regulatory hurdles. Most LHS initiatives lacked discussion on long-term economic sustainability models, and only 16 papers provided objective measurements of performance changes. Drawing from the findings of the included studies, this paper offers recommendations for the effective implementation of the LHS.</div></div><div><h3>Conclusion</h3><div>The establishment of sustainable LHS necessitates several key components. First, there is a need to develop long-term economic sustainability models. Secondly, governance at the national level should promote common Application Programming Interfaces (APIs) across LHS implementations, communication channels to share tacit knowledge, efficient Institutional Review Board, ethical approval processes, and connect various initiatives currently operating independently. Lastly, the success of LHS relies not only on technological infrastructure but also on the active participation of multidisciplinary teams in decision-making and sharing of tacit knowledge.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hospital antimicrobial stewardship team perceptions and usability of a computerized clinical decision support system","authors":"","doi":"10.1016/j.ijmedinf.2024.105653","DOIUrl":"10.1016/j.ijmedinf.2024.105653","url":null,"abstract":"<div><h3>Background</h3><div>Antimicrobial stewardship (AMS) programs aim to optimize antibiotic use through a panel of interventions. The implementation of computerized clinical decision support systems (CDSSs) offers new opportunities for semiautomated antimicrobial review by AMS teams. This study aimed to evaluate the perceived facilitators, barriers and benefits of end-users related to a commercial CDSS recently implemented in a hospital and to assess its usability.</div></div><div><h3>Methods</h3><div>A mixed-method approach was used among AMS team members nine months after the implementation of the CDSS in a university hospital in northeastern France. A qualitative analysis based on individual semistructured interviews was conducted to collect end-users’ perceptions. A quantitative analysis was performed using the System Usability Scale (SUS).</div></div><div><h3>Results</h3><div>Eleven AMS team members agreed to participate. The qualitative analysis revealed technical, organizational and human barriers and facilitators of CDSS implementation. Effective collaboration with information technology teams was crucial for ensuring the installation and configuration of the software. CDSS adoption by the AMS team required time, human resources, training, adaptation and a clinical leader. Moreover, the CDSS had to be well designed, user-friendly and provide benefits to AMS activities. The quantitative analysis indicated that the CDSS was a “good” system in terms of perceived ease of use (median SUS score: 77.5/100).</div></div><div><h3>Conclusions</h3><div>This study shows the value of the studied CDSS to support AMS activities. It reveals barriers, facilitators and benefits to the implementation and adoption of the CDSS. These barriers and facilitators could be considered to facilitate the implementation of the software in other hospitals.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and expert inspections of the user interface for a primary care decision support system","authors":"","doi":"10.1016/j.ijmedinf.2024.105651","DOIUrl":"10.1016/j.ijmedinf.2024.105651","url":null,"abstract":"<div><h3>Background</h3><div>General practitioners play a unique key role in diagnosing patients with unclear diseases. Decision support systems in primary care can assist with diagnosis provided that they are efficient and user-friendly.</div></div><div><h3>Objectives</h3><div>The objective of this study is to develop a high-fidelity prototype of the user interface of a clinical decision support system for primary care, particularly for diagnosis support in unclear diseases, using expert inspections at an early stage of development to ensure a high level of usability.</div></div><div><h3>Methods</h3><div>The user interface prototype was iteratively developed based on previous research, design principles, and usability guidelines. During the development phase, three usability inspections were carried out by all experts at four-week intervals as heuristic walkthrough. Each inspection consisted of two parts: 1) Task-based inspection 2) Free exploration and evaluation based on usability heuristics. Five domain experts assessed the current status of development.</div><div>The tasks in the inspections were based on the task model derived in the requirements analysis: perform data entry, review and discuss results, schedule further diagnostics, refer to specialists and close case.</div></div><div><h3>Results</h3><div>As a result of this iterative development, a high-fidelity, clickable user interface prototype was created that is able to fulfil all six tasks of our task model. The usability inspections identified a total of 196 usability issues (for all 3 inspections; Part 1: 90 issues, Part 2: 106 issues), ranging in severity from minor to severe. These served the continuous adjustment and improvement of the prototype. All main tasks were completed successfully despite these problems.</div></div><div><h3>Conclusion</h3><div>Usability inspections through heuristic walkthroughs can support and optimise the development of a user-centred decision support system in order to ensure its suitability for performing relevant tasks.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From challenges to opportunities: Digital transformation in hospital-at-home care","authors":"","doi":"10.1016/j.ijmedinf.2024.105644","DOIUrl":"10.1016/j.ijmedinf.2024.105644","url":null,"abstract":"<div><h3>Background</h3><div>Digital transformation is an ongoing sosio-technological process that can create opportunities in the health sector. However, the current landscape of digital transformation in hospital-at-home care is unknown.</div></div><div><h3>Aim</h3><div>To describe healthcare providers’ perspectives of digital transformation in hospital-at-home care.</div></div><div><h3>Methods</h3><div>A total of 25 semi-structured interviews were conducted in September-October 2023 in all Finnish wellbeing services counties (n = 21), the city of Helsinki (n = 1), and private health care providers (n = 3). Snowball sampling was used (N = 46). The data underwent an inductive content analysis.</div></div><div><h3>Result</h3><div>The analysis revealed four main and 17 generic categories of challenges and opportunities of digital transformation in hospital-at-home care. These challenges and opportunities were related to 1) Health information exchange in and across hospital-at-home care; 2) Management of hospital-at-home care; 3) Logistics in hospital-at-home care planning and delivery; and 4) Digital health interventions in hospital-at-home care delivery.</div></div><div><h3>Conclusions</h3><div>The challenges and opportunities of digital transformation in the hospital-at-home care is intricately linked to the efficiency of health information exchange, management, logistics, and digital health interventions. Addressing the key areas of improvement in health information exchange can lead to more streamlined patient care processes and improved communication between healthcare professionals and patients. Digital transformation in management and logistics can improve overall efficiency within healthcare systems. Digital health interventions may promote equitable and universal access to high-quality healthcare. Continued focus on health care information infrastructure, in particular interoperability of electronic health records and optimization of information flow, will be essential to realize the full potential of digitalization.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142407212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Barriers, facilitators, and recommendations to increase the use of a clinical decision support tool for managing chronic pain in primary care","authors":"","doi":"10.1016/j.ijmedinf.2024.105649","DOIUrl":"10.1016/j.ijmedinf.2024.105649","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Primary care providers (PCPs) use poorly organized patient information in electronic health records (EHR) within a limited time when treating patients with chronic pain. Clinical decision support (CDS) tools assist PCPs by synthesizing patient information and prompting guideline-concordant treatment decisions. A CDS tool- Chronic Pain OneSheet was developed through a user-centered design process to support PCP’s decision-making for patients with chronic noncancer pain. OneSheet aggregates relevant patient information in one place in the EHR. OneSheet also guides PCPs in completing guideline-recommended opioid risk management tasks, tracking patient treatments, and documenting pain-related symptoms. Our objective was to identify barriers, facilitators, and recommendations to increase OneSheet use for chronic noncancer pain management in primary care.</div></div><div><h3>Methods</h3><div>We conducted 19 qualitative interviews with PCPs from two academic health systems who had access to OneSheet in their EHR. Interview transcripts were coded to identify common themes using a modified thematic approach.</div></div><div><h3>Results</h3><div>PCPs identified several barriers to using OneSheet, including limited time to address patient needs associated with multiple chronic conditions, resistance to changing established workflows, and complex OneSheet display. PCPs reported several facilitators to using OneSheet, such as OneSheet’s ability to serve as a hub for chronic pain data, easy access to features that facilitate completing mandatory tasks and improved planning for certain patient visits. PCPs recommended prioritizing access to commonly used features, adding display customization capabilities, and expanding access to patients and other team members to increase OneSheet use.</div></div><div><h3>Conclusion</h3><div>Our findings highlight the importance of acknowledging the PCP workflow and task load when designing CDS tools. Future CDS tools should balance the extent of information provided with assisting PCPs to fulfill mandatory tasks. Expanding CDS tools to multiple care team members and patients can also lead to higher use by facilitating data entry, leading to more streamlined care delivery.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pathologist-level diagnosis of ulcerative colitis inflammatory activity level using an automated histological grading method","authors":"","doi":"10.1016/j.ijmedinf.2024.105648","DOIUrl":"10.1016/j.ijmedinf.2024.105648","url":null,"abstract":"<div><h3>Background and Aims</h3><div>Inflammatory bowel disease (IBD) is a global disease that is evolving with increasing incidence. However, there are few works on computationally assisted diagnosis of IBD based on pathological images. Therefore, based on the UK and Chinese IBD diagnostic guidelines, our study established an artificial intelligence-assisted diagnostic system for histologic grading of inflammatory activity in ulcerative colitis (UC).</div></div><div><h3>Methods</h3><div>We proposed an efficient deep-learning (DL) method for grading inflammatory activity in whole-slide images (WSIs) of UC pathology. Our model was constructed using 603 UC WSIs from Nanjing Drum Tower Hospital for model train set and internal test set. We collected 212 UC WSIs from Zhujiang Hospital as an external test set. Initially, the pre-trained ResNet50 model on the ImageNet dataset was employed to extract image patch features from UC patients. Subsequently, a multi-instance learning (MIL) approach with embedded self-attention was utilized to aggregate tissue image patch features, representing the entire WSI. Finally, the model was trained based on the aggregated features and WSI annotations provided by senior gastrointestinal pathologists to predict the level of inflammatory activity in UC WSIs.</div></div><div><h3>Results</h3><div>In the task of distinguishing the presence or absence of inflammatory activity, the Area Under Curve (AUC) value in the internal test set is 0.863 (95% confidence interval [CI] 0.829, 0.898), with a sensitivity of 0.913 (95% [CI] 0.866, 0.961), and specificity of 0.816 (95% [CI] 0.771, 0.861). The AUC in the external test set is 0.947 (95% confidence interval [CI] 0.939, 0.955), with a sensitivity of 0.889 (905% [CI] 0.837, 0.940), and specificity of 0.858 (95% [CI] 0.777, 0.939). For distinguishing different levels of inflammatory activity in UC, the average Macro-AUC in the internal test set and the external test set are 0.827 (95% [CI] 0.803, 0.850) and 0.908 (95% [CI] 0.882, 0.935). the average Micro-AUC in the internal test set and the external test set are 0.816 (95% [CI] 0.792, 0.840) and 0.898 (95% [CI] 0.869, 0.926).</div></div><div><h3>Conclusions</h3><div>Comparative analysis with diagnoses made by pathologists at different expertise levels revealed that the algorithm reached a proficiency comparable to the pathologist with 5 years of experience. Furthermore, our algorithm performed superior to other MIL algorithms.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142425742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of pre-eclampsia with machine learning approaches: Leveraging important information from routinely collected data","authors":"","doi":"10.1016/j.ijmedinf.2024.105645","DOIUrl":"10.1016/j.ijmedinf.2024.105645","url":null,"abstract":"<div><h3>Background</h3><div>Globally, pre-eclampsia (PE) is a leading cause of maternal and perinatal morbidity and mortality. PE prediction using routinely collected data has the advantage of being widely applicable, particularly in low-resource settings. Early intervention for high-risk women might reduce PE incidence and related complications. We aimed to replicate our machine learning (ML) published work predicting another maternal condition (gestational diabetes) to (1) predict PE using routine health data, (2) identify the optimal ML model, and (3) compare it with logistic regression approach.</div></div><div><h3>Methods</h3><div>Data were from a large health service network with 48,250 singleton pregnancies between January 2016 and June 2021. Supervised ML models were employed. Maternal clinical and medical characteristics were the feature variables (predictors), and a 70/30 data split was used for training and testing the model. Predictive performance was assessed using area under the curve (AUC) and calibration plots. Shapley value analysis assessed the contribution of feature variables.</div></div><div><h3>Results</h3><div>The random forest approach provided excellent discrimination with an AUC of 0.84 (95% CI: 0.82–0.86) and highest prediction accuracy (0.79); however, the calibration curve (slope of 1.21, 95% CI 1.13–1.30) was acceptable only for a threshold of 0.3 or less. The next best approach was extreme gradient boosting, which provided an AUC of 0.77 (95% CI: 0.76–0.79) and well-calibrated (slope of 0.93, 95% CI 0.85–1.01). Logistic regression provided good discrimination performance with an AUC of 0.75 (95% CI: 0.74–0.76) and perfect calibration. Nulliparous, pre-pregnancy body mass index, previous pregnancy with prior PE, maternal age, family history of hypertension, and pre-existing hypertension and diabetes were the top-ranked features in Shapley value analysis.</div></div><div><h3>Conclusion</h3><div>Two ML models created the highest-performing prediction using routinely collected data to identify women at high risk of PE, with acceptable discrimination. However, to confirm this result and also examine model generalisability, external validation studies are needed in other settings, utilising standardised prognostic factors.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142407214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CureMate: A clinical decision support system for breast cancer treatment","authors":"","doi":"10.1016/j.ijmedinf.2024.105647","DOIUrl":"10.1016/j.ijmedinf.2024.105647","url":null,"abstract":"<div><h3>Background</h3><div>Breast Cancer (BC) poses significant challenges in treatment decision-making. Multiple first treatment lines are currently available, determined by several patient-specific factors that need to be considered in the decision-making process.</div></div><div><h3>Purpose</h3><div>To present CureMate, a Clinical Decision Support System to predict the most effective initial treatment for BC patients. Different artificial intelligence models based on demographic, anatomopathological and magnetic resonance imaging variables are studied. CureMate’s web application allows for easy use of the best model.</div></div><div><h3>Methods</h3><div>A database of 232 BCE patients, each described by 29 variables, was established. Out of four machine learning algorithms, specifically Decision Tree Classifier (DTC), Gaussian Naïve Bayes (GNB), k-Nearest Neighbor (K-NN), and Support Vector Machine (SVM), the most suitable model for the task was identified, optimized and independently tested.</div></div><div><h3>Results</h3><div>SVM was identified as the best model for BC treatment planning, resulting in a test accuracy of 0.933. CureMate’s web application, including the SVM model, allows for introducing the relevant patient variables and displays the suggested first treatment step, as well as a diagram of the following steps.</div></div><div><h3>Conclusion</h3><div>The results demonstrate CureMate’s high accuracy and effectiveness in clinical settings, indicating its potential to aid practitioners in making informed therapeutic decisions.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142407211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HERALD: A domain-specific query language for longitudinal health data analytics","authors":"","doi":"10.1016/j.ijmedinf.2024.105646","DOIUrl":"10.1016/j.ijmedinf.2024.105646","url":null,"abstract":"<div><h3>Background</h3><div>Large-scale health data has significant potential for research and innovation, especially with longitudinal data offering insights into prevention, disease progression, and treatment effects. Yet, analyzing this data type is complex, as data points are repeatedly documented along the timeline. As a consequence, extracting cross-sectional tabular data suitable for statistical analysis and machine learning can be challenging for medical researchers and data scientists alike, with existing tools lacking balance between ease-of-use and comprehensiveness.</div></div><div><h3>Objective</h3><div>This paper introduces HERALD, a novel domain-specific query language designed to support the transformation of longitudinal health data into cross-sectional tables. We describe the basic concepts, the query syntax, a graphical user interface for constructing and executing HERALD queries, as well as an integration into Informatics for Integrating Biology and the Bedside (i2b2).</div></div><div><h3>Methods</h3><div>The syntax of HERALD mimics natural language and supports different query types for selection, aggregation, analysis of relationships, and searching for data points based on filter expressions and temporal constraints. Using a hierarchical concept model, queries are executed individually for the data of each patient, while constructing tabular output. HERALD is closed, meaning that queries process data points and generate data points. Queries can refer to data points that have been produced by previous queries, providing a simple, but powerful nesting mechanism.</div></div><div><h3>Results</h3><div>The open-source implementation consists of a HERALD query parser, an execution engine, as well as a web-based user interface for query construction and statistical analysis. The implementation can be deployed as a standalone component and integrated into self-service data analytics environments like i2b2 as a plugin. HERALD can be valuable tool for data scientists and machine learning experts, as it simplifies the process of transforming longitudinal health data into tables and data matrices.</div></div><div><h3>Conclusion</h3><div>The construction of cross-sectional tables from longitudinal data can be supported through dedicated query languages that strike a reasonable balance between language complexity and transformation capabilities.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142407213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}