International Journal of Medical Informatics最新文献

{"title":"Association between the platelet to white blood cell ratio and short term mortality in critically ill patients with atherosclerotic cardiovascular disease: A retrospective study and machine learning with external validation","authors":"Zhantao Cao ,&nbsp;Hewei Qin ,&nbsp;Yue Niu ,&nbsp;Zilu Zhang ,&nbsp;Guoju Dong","doi":"10.1016/j.ijmedinf.2026.106267","DOIUrl":"10.1016/j.ijmedinf.2026.106267","url":null,"abstract":"<div><h3>Background</h3><div>The platelet to white blood cell ratio (PWR) has shown prognostic value in many diseases. Yet its predictive utility for patients with atherosclerotic cardiovascular disease (ASCVD) who receive care in the intensive care unit (ICU) remains uncertain. We examined whether PWR at ICU admission is associated with short term all cause mortality among ICU patients with ASCVD.</div></div><div><h3>Methods</h3><div>We used the MIMIC IV and eICU databases to study the association between PWR and 30 day all cause mortality in critically ill patients with ASCVD. Patients were grouped by PWR quartiles. Collinearity was checked with the variance inflation factor (VIF). Nonlinearity was assessed with restricted cubic splines(RCS). Survival was compared with Kaplan Meier(KM) curves and the log rank test. Hazard ratios were estimated with stratified and adjusted Cox models. We also built machine learning models that included PWR and clinical features selected with the Boruta algorithm to predict 30 day mortality. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC). External validation in the eICU database was used to assess generalizability.</div></div><div><h3>Results</h3><div>A total of 10 943 ICU patients with ASCVD were included, 62 % were men, and the median age was 71 years. The highest PWR quartile had a lower 30 day all cause mortality than the lowest quartile, 11 % versus 15 % (<em>p</em> &lt; 0.001). In multivariable Cox models the highest quartile had a lower risk (HR 0.80, 95 % CI 0.67 to 0.95, <em>p</em> = 0.012). RCS suggested a nonlinear association. Age modified the association, with a stronger protective effect in patients younger than 70 years (HR 0.64, 95 % CI 0.47 to 0.87, interaction <em>p</em> &lt; 0.001). The best machine learning model achieved an AUC of 0.812 in internal validation and 0.80 in external validation. SHAP analysis showed that higher PWR was linked to a lower predicted risk of death.</div></div><div><h3>Conclusion</h3><div>PWR independently predicts 30 day all cause mortality in ICU patients with ASCVD. These findings support the use of PWR for risk stratification and to inform management in critical care for ASCVD.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106267"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145936485","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":"Leveraging large language models to automate the identification of healthcare access barriers for veterans","authors":"Sudarshan Srinivasan ,&nbsp;Caitlin Rizy ,&nbsp;Maria Mahbub ,&nbsp;David Bolme ,&nbsp;Alina Peluso ,&nbsp;Jodie Trafton ,&nbsp;Ioana Danciu","doi":"10.1016/j.ijmedinf.2025.106247","DOIUrl":"10.1016/j.ijmedinf.2025.106247","url":null,"abstract":"<div><h3>Objective</h3><div>To develop and evaluate an automated system for identifying healthcare barriers focusing on transportation issues in veterans’ clinical notes using large language models (LLMs) and to assess the impact of different prompting strategies on classification performance and explanation consistency.</div></div><div><h3>Methods</h3><div>We developed a hybrid system combining pattern matching for templated notes with LLM analysis for free-text notes. Using 2000 manually annotated clinical notes, we compared four prompting strategies (dual-role short, dual-role long, analysis-first, analysis-only) across Mistral-7B and Llama-3.1 models. We evaluated classification performance using standard metrics and assessed explanation consistency through embedding similarity analysis.</div></div><div><h3>Results</h3><div>The analysis-first strategy achieved superior performance, with Mistral-7B reaching an F1 score of 0.914, outperforming traditional machine learning approaches (GBM: 0.786, BERT: 0.811). LLMs demonstrated higher explanation consistency within models (mean cosine similarity 0.887–0.908) compared to cross-model similarities (0.767–0.872). Pattern matching successfully handled 6.7% of templated notes deterministically. Mistral-7B showed greater internal consistency but higher abstention rates compared to Llama-3.1.</div></div><div><h3>Conclusion</h3><div>Requiring LLMs to analyze evidence before classification improves both accuracy and explanation consistency for identifying transportation barriers in clinical notes. This approach enables automated barrier detection at scale while providing clinically relevant explanations, supporting both population-level healthcare planning and individual patient care decisions.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106247"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145936217","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":"A topic modeling analysis of stigma dimensions, social, and related behavioral circumstances in clinical notes among patients with HIV","authors":"Ziyi Chen ,&nbsp;Yiyang Liu ,&nbsp;Mattia Prosperi ,&nbsp;Krishna Vaddiparti ,&nbsp;Robert L. Cook ,&nbsp;Jiang Bian ,&nbsp;Yi Guo ,&nbsp;Yonghui Wu","doi":"10.1016/j.ijmedinf.2026.106269","DOIUrl":"10.1016/j.ijmedinf.2026.106269","url":null,"abstract":"<div><h3>Objective</h3><div>To characterize stigma dimensions, social, and related behavioral circumstances in people living with HIV (PLWHs) seeking care, using natural language processing methods applied to a large collection of electronic health record (EHR) clinical notes from a large integrated health system in the southeast United States.</div></div><div><h3>Methods</h3><div>We identified a cohort of PLWHs from the University of Florida (UF) Health Integrated Data Repository and performed topic modeling analysis using Latent Dirichlet Allocation (LDA) to uncover stigma-related dimensions and related social and behavioral contexts. Domain experts created a seed list of HIV-related stigma keywords, then applied a snowball strategy to iteratively review notes for additional terms until saturation was reached. To identify more target topics, we tested three keyword-based filtering strategies. The detected topics were evaluated using three widely used metrics and manually reviewed by specialists. Word frequency analysis was used to highlight the prevalent terms associated with each topic. In addition, we conducted topic variation analysis among subgroups to examine differences across age- and sex-specific demographics.</div></div><div><h3>Results</h3><div>We identified 9,140 PLWHs at UF Health and collected 2.9 million clinical notes. Through the iterative keyword approach, we generated a list of 91 keywords associated with HIV-related stigma. Topic modeling on sentences containing at least one keyword uncovered a wide range of topic themes associated with HIV-related stigma, social, and related behaviors circumstances, including “Mental Health Concern and Stigma”, “Social Support and Engagement”, “Limited Healthcare Access and Severe Illness”, “Missed Appointments and HIV Care Monitoring”, “Treatment Refusal and Isolation”, “Intimate Partner Violence and Relationship Concerns”, “Fear of Falling and Physical Health Concerns”, “Substance Abuse”, and “Food Insecurity and Resource Scarcity”. Topic variation analysis across sex and age subgroups revealed no substantial difference between males and females; however, there were differences were observed among different ages. For example, “Fear of Falling and Physical Health Concerns” was notably more prevalent among older adults.</div></div><div><h3>Conclusion</h3><div>Extracting and understanding the HIV-related stigma and associated social and behavioral circumstances from EHR clinical notes enables scalable, time-efficient assessment and overcoming the limitations of traditional questionnaires. Findings from this research provide actionable insights to inform patient care and interventions to improve HIV-care outcomes.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106269"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940467","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":"Predicting emergency mortality risk in traumatic brain injury: comparative analysis of machine learning and large language model GPT-5","authors":"Kuan-Chi Tu ,&nbsp;Yung-De Kuo ,&nbsp;Tee-Tau Eric Nyam ,&nbsp;Yu-Shan Ma ,&nbsp;Mei-I Sung ,&nbsp;Chung-Feng Liu ,&nbsp;Ching-Lung Kuo","doi":"10.1016/j.ijmedinf.2026.106268","DOIUrl":"10.1016/j.ijmedinf.2026.106268","url":null,"abstract":"<div><h3>Background</h3><div>Artificial intelligence (AI) has become increasingly important in predicting outcomes of traumatic brain injury (TBI). Traditional machine learning (ML) models, such as support vector machines (SVMs), have shown high accuracy, whereas the potential of large language models (LLMs) in structured clinical prediction remains underexplored.</div></div><div><h3>Purpose</h3><div>This study compared the predictive performance of ML and LLM approaches (ChatGPT-5 with Thinking response mode) using the same TBI dataset and evaluated the impact of prompting strategies and threshold calibration on model reliability and clinical applicability.</div></div><div><h3>Methods</h3><div>A dataset of 5,475 TBI cases with 12 clinical features was used to build an SVM model and four LLM strategies: zero-shot GPT, few-shot GPT, few-shot + chain-of-thought (CoT) GPT, and CoT-only GPT. Performance was evaluated by accuracy, sensitivity, specificity, and ROC-AUC under fixed and balanced thresholds.</div></div><div><h3>Results</h3><div>The SVM model achieved the best overall performance (AUC = 0.920). After threshold adjustment, all LLMs reached comparable AUCs (0.902–0.919). Few-shot GPT most closely matched SVM, CoT + few-shot achieved highest sensitivity, and CoT-only favored specificity.</div></div><div><h3>Conclusion</h3><div>Proper threshold calibration enables LLMs to approximate ML accuracy while offering rapid deployment and interpretability. Prompt engineering combined with adaptive cut-off tuning may enhance clinical usability of LLM-based prediction systems.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106268"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145918986","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":"Beyond binary diagnosis: Key questions on AI accuracy, real-world applicability, and safety in clinical decision support","authors":"Jin Ye","doi":"10.1016/j.ijmedinf.2026.106292","DOIUrl":"10.1016/j.ijmedinf.2026.106292","url":null,"abstract":"<div><div>This comment relates to Kücking et al.’s (2026) study on the bidirectional effects of artificial intelligence recommendations and healthcare provider related factors on the accuracy of wound impregnation diagnosis. While acknowledging the valuable contributions of this research, including distinguishing between correct/incorrect artificial intelligence outputs, rigorous simulation design, and emphasis on clinical safety, we have raised key questions to enhance the interpretation of results and real-world translation. The main focuses include the moderating role of artificial intelligence system accuracy in automation bias, external effectiveness in real clinical environments, potential mechanisms for gender differences in diagnostic performance, the impact of visual cue design on decision-making, and the potential of explainable artificial intelligence (XAI) in risk mitigation. This review aims to promote further research and facilitate the safe and effective integration of artificial intelligence based clinical decision support systems (CDSS) into clinical practice.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106292"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146013323","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":"Development and validation of data-driven, decision tree–based algorithms for identifying Behçet’s disease in claims data","authors":"Ken-ei Sada ,&nbsp;Yoshia Miyawaki ,&nbsp;Ryo Yanai ,&nbsp;Takashi Kida ,&nbsp;Akira Onishi ,&nbsp;Ryusuke Yoshimi ,&nbsp;Kunihiro Ichinose ,&nbsp;Yasuhiro Shimojima","doi":"10.1016/j.ijmedinf.2026.106266","DOIUrl":"10.1016/j.ijmedinf.2026.106266","url":null,"abstract":"<div><h3>Objective</h3><div>To develop and externally validate novel, data-driven algorithms that are based on appropriate variable selection methods for identifying patients with Behçet’s disease in Japan.</div></div><div><h3>Methods</h3><div>This retrospective cross-sectional study included 13,538 patients from six tertiary hospitals (November–December 2023). One year of claims data was linked to chart-confirmed Behçet’s disease diagnoses. Patients were randomly divided into training (n = 8,811) and test (n = 3,775) sets, with external validation (n = 952) from another hospital. Feature selection among Behçet’s disease-coded patients used the Least Absolute Shrinkage and Selection Operator, Boruta, and Recursive Feature Elimination. The diagnostic performance of the rule-based algorithms, which were derived from the decision tree models, was evaluated using accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value, and F1 score.</div></div><div><h3>Results</h3><div>Diagnosis codes alone achieved high sensitivity (1.000) and specificity (0.992) but modest PPV (0.767, test set; 0.850, external validation). Incorporating sulphamethoxazole–trimethoprim and colchicine prescriptions improved the positive predictive value, which was 0.793 in the test set and 0.865 in external validation.</div></div><div><h3>Conclusion</h3><div>Incorporating prescriptions alongside diagnosis codes improved PPV while maintaining high sensitivity and specificity. Building upon a data-driven framework that integrates variable selection methods and decision tree analysis, this study provides a validated and scalable approach for reliable claims-based research on Behçet’s disease.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106266"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145936509","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":"Incorporating patient history into the insulin sensitivity prediction in intensive care by feedforward neural network models","authors":"Bálint Szabó ,&nbsp;J.Geoffrey Chase ,&nbsp;Balázs Benyó","doi":"10.1016/j.ijmedinf.2026.106273","DOIUrl":"10.1016/j.ijmedinf.2026.106273","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Insulin sensitivity prediction is crucial for model-based treatment in Intensive Care Unit patients, particularly those with hyperglycemia. However, predicting insulin sensitivity is challenging due to inter- and intra-patient variability.</div></div><div><h3>Methods</h3><div>Different neural network models are proposed and compared for predicting insulin sensitivity, including recurrent and feedforward versions of the Classification Deep Neural Network and Mixture Density Network models. These models were trained using 1879 patient records containing 123,988 insulin sensitivity values from three intensive care patient cohorts in three different countries.</div></div><div><h3>Results</h3><div>Results show that using patient history in prediction models can improve the accuracy of insulin sensitivity predictions. The Mixture Density Network model provided more accurate predictions, measured by a problem-specific metric that expresses clinical requirements. We demonstrated that even using up to 12 h of historical data can improve prediction accuracy.</div></div><div><h3>Conclusion</h3><div>This study highlights the potential of recurrent neural network models in predicting insulin sensitivity in Intensive Care Unit patients. Our findings suggest that using recurrent neural networks and incorporating patient history can lead to more accurate predictions. These results are generalizable due to the large and diverse dataset employed, which included patients from three different cohorts in three care settings.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106273"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146020812","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":"Communicable diseases platform (CDP): Real-Time clinical analytics for infections","authors":"Manuri De Silva ,&nbsp;Alice Voskoboynik ,&nbsp;Sailavan Ramesh ,&nbsp;Janice Campbell ,&nbsp;Saravanan Satkumaran ,&nbsp;Daryl R. Cheng","doi":"10.1016/j.ijmedinf.2026.106277","DOIUrl":"10.1016/j.ijmedinf.2026.106277","url":null,"abstract":"<div><h3>Objective</h3><div>Communicable diseases, especially seasonal respiratory illnesses, contribute significantly to paediatric hospital presentations and admissions. Existing surveillance systems often require retrospective manual data collation and focus on either demographic or clinical data, not both. The Communicable Diseases Platform (CDP) is a dynamic data platform that aggregates both data types for all communicable disease presentations to The Royal Children’s Hospital Melbourne (RCH).</div></div><div><h3>Methods</h3><div>In the pilot phase, the CDP extracted de-identified aggregated data from hospital electronic medical records for patients with positive respiratory swabs. A dashboard displayed positivity rate and cumulative hospital admissions trends from 2016 to 2025, further filterable by pathogen, age, presentation type and interventions.</div></div><div><h3>Discussion</h3><div>The CDP improves understanding of clinical profiles, disease burden and seasonal patterns, supporting better outbreak control, patient flow prediction and clinical surveillance. Future developments include immunisation data integration and machine learning algorithm evaluation for real-time vaccine effectiveness estimations and communicable disease predictive modelling.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106277"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979478","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":"“Calibration or contamination?” Reassessing the evaluation of large language models for clinical mortality prediction","authors":"Zhihao Lei","doi":"10.1016/j.ijmedinf.2026.106291","DOIUrl":"10.1016/j.ijmedinf.2026.106291","url":null,"abstract":"","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106291"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979480","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":"An old disease, a new linguistic challenge for large language models: patient education on psoriasis and psoriatic arthritis in an underrepresented medical language","authors":"Ahmet Ugur Atilan,&nbsp;Niyazi Cetin","doi":"10.1016/j.ijmedinf.2025.106246","DOIUrl":"10.1016/j.ijmedinf.2025.106246","url":null,"abstract":"<div><h3>Objective</h3><div>Large Language Models (LLMs) are increasingly applied to patient education, yet their performance in languages that are relatively underrepresented in medical-domain corpora and large language model training datasets remains underexplored. Psoriasis and psoriatic arthritis (PsA) are chronic, immune-mediated diseases requiring lifelong patient engagement, making them suitable conditions to evaluate the clarity, reliability, and inclusivity of AI-generated educational content. To assess the comprehensibility, scientific reliability, and patient-centered communication of Turkish patient education materials for psoriasis vulgaris and PsA generated by seven state-of-the-art LLMs.</div></div><div><h3>Methods</h3><div>A cross-sectional analysis compared outputs from ChatGPT-4o, Gemini 2.0 Flash, Claude 3.7 Sonnet, Grok 3, Qwen 2.5, DeepSeek R1, and Mistral Large 2. Brochures were produced using standardized zero-shot prompts and evaluated via the Ateşman readability index and the DISCERN instrument. Overall differences in DISCERN scores across the seven models were assessed using a Friedman test, followed by Bonferroni-adjusted Wilcoxon signed-rank post-hoc analyses.</div></div><div><h3>Results</h3><div>Readability scores ranged from 61.6 to 80.2 (mean = 71.3 ± 6.9), with ChatGPT-4o and Qwen 2.5 generating the most accessible texts. DISCERN reliability scores ranged from 38.5 to 60.5, with Claude 3.7 Sonnet and Gemini 2.0 Flash showing the highest accuracy. Models prioritizing factual precision produced denser language, while conversational models favored fluency but sacrificed depth. Notable variation was observed, with only Claude 3.7 Sonnet and Gemini 2.0 Flash consistently reflecting patient-centered perspectives.</div></div><div><h3>Conclusion</h3><div>LLMs showed observable differences in balancing clarity and reliability when generating health education leaflets in Turkish. Most outputs appeared to lack explicit psychosocial framing and emphasis on shared decision-making, which may suggest the need for more culturally adaptive training, clinician oversight, and locally grounded validation frameworks to support safe and inclusive AI-based patient education.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106246"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979482","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}