International Journal of Medical Informatics最新文献

{"title":"Interpretable machine learning model for predicting in-hospital mortality in elderly acute pancreatitis: Development and validation in a multicenter cohort","authors":"Hao He ,&nbsp;Li Luo ,&nbsp;Lei Bai ,&nbsp;Lei Luo ,&nbsp;Kunming Tian ,&nbsp;Xiaoyun Fu ,&nbsp;Bao Fu","doi":"10.1016/j.ijmedinf.2026.106299","DOIUrl":"10.1016/j.ijmedinf.2026.106299","url":null,"abstract":"<div><h3>Background</h3><div>Elderly acute pancreatitis (AP) patients face significantly higher in-hospital all-cause mortality, highlighting the need for effective risk stratification to support timely clinical decision-making.</div></div><div><h3>Methods</h3><div>We conducted a multicenter retrospective study that enrolled 2,728 elderly AP patients, with which we developed and validated a robust machine learning (ML) model for predicting in-hospital all-cause mortality. We first selected predictors of mortality using LASSO regression and random forest–based Boruta algorithms. Then, seven ML models incorporating the selected predictors were trained and evaluated using the area under the receiver operating characteristic curve (AUC).</div></div><div><h3>Results</h3><div>XGBoost demonstrated the highest predictive performance, achieving an AUC of 0.884 (95% CI: 0.823–0.945) in the external validation test, outperforming the conventional Ranson score in predicting in-hospital mortality. Shapley additive explanations ranked vasoactive drug, hospital length of stay, leukocyte count, noninvasive ventilation, and invasive mechanical ventilation as five key predictors. An interactive web-based tool based on the optimal XGBoost model has been available at <span><span>https://appredction.shinyapps.io/acutepancreatitis_xgb/</span><svg><path></path></svg></span> to generate real-time risk predictions.</div></div><div><h3>Conclusions</h3><div>This study proposed a validated and interpretable ML model to support in-hospital risk stratification for elderly patients with AP, thereby facilitating clinical decision-making and optimizing intensive care unit resource allocation.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106299"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146031675","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":"Mapping clinical terms to standard terminology for multi-institutional research platform: Mapping principles and system deployment","authors":"Hannah Kang ,&nbsp;Youngsun Park ,&nbsp;Yukyeong Son ,&nbsp;Ho-Young Lee ,&nbsp;Soo-Yong Shin","doi":"10.1016/j.ijmedinf.2026.106294","DOIUrl":"10.1016/j.ijmedinf.2026.106294","url":null,"abstract":"<div><h3>Objective</h3><div>To develop and implement a multi-institutional research platform by standardizing and integrating clinical terms with international terminologies.</div></div><div><h3>Materials and Methods</h3><div>This study introduces the Health data Research Suite (HRS) platform, designed to standardize and connect electronic medical record (EMR) data across institutions for efficient multi-institutional research. A hybrid mapping process—combining automated and manual methods—ensures semantic equivalency, consistency, and compliance with international standards like SNOMED CT, LOINC, and RxNorm. Key strategies included domain-specific semantic restrictions, prioritized attributes for post-coordination, and tailored mapping approaches. Cross-validation and expert consultations resolved mapping discrepancies and inactive concept issues, ensuring reliable data alignment.</div></div><div><h3>Results</h3><div>The study enhanced mapping accuracy in SNOMED CT, LOINC, and RxNorm by utilizing semantic tags and attribute prioritization, with expert consultations addressing any discrepancies. The HRS platform, designed with advanced code search capabilities and user-friendly interfaces, improved cohort generation and facilitated multi-institutional research.</div></div><div><h3>Discussion</h3><div>Challenges in maintaining inter-institutional consistency and addressing SNOMED CT’s hierarchical limitations were mitigated with a detailed mapping manual, systematic validation, and expert consensus-building. However, a national shortage of trained terminology specialists in Korea underscores the need for educational programs to enhance workforce expertise. Future enhancements include advanced search options and attribute-based retrieval to further improve usability and research support.</div></div><div><h3>Conclusion</h3><div>This study presents a mapping strategy to align institutional clinical terms with international standards, addressing challenges in semantic consistency and system implementation. The approach enhances multi-institutional research efficiency and fosters innovation in integrated healthcare research, with potential to advance global health outcomes.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106294"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146020826","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":"Empowering caregivers of individuals with autism spectrum disorder through sensor-based monitoring of emotional dysregulation: A scoping review","authors":"Moid Sandhu ,&nbsp;Siddique Latif ,&nbsp;Andrew Bayor ,&nbsp;Wei Lu ,&nbsp;Mahnoosh Kholghi ,&nbsp;Deepa Prabhu ,&nbsp;David Silvera-Tawil","doi":"10.1016/j.ijmedinf.2026.106262","DOIUrl":"10.1016/j.ijmedinf.2026.106262","url":null,"abstract":"<div><div><em>Objective:</em> This paper critically reviews existing work in sensor-based emotional dysregulation monitoring to support caregivers of individuals diagnosed with autism spectrum disorder (ASD).</div><div><em>Methods:</em> A systematic literature search was conducted across six databases (Google Scholar, IEEE Xplore, Scopus, ACM Digital Library, Web of Science, and PubMed) covering publications from January 1, 2016, to September 30, 2025.</div><div><em>Results:</em> Thirty-two studies met inclusion criteria, comprising 27 focused on sensor-based emotional dysregulation detection and 5 addressing intervention or support mechanisms. These studies suggest that sensor-based technologies have potential for continuous physiological monitoring, facilitating early detection and intervention to support emotional dysregulation episodes. Critical deficiencies were identified in real-time alerting capabilities, autonomous intervention deployment, self-regulation framework integration, system reliability, long-term sustainability, user interface design, and cross-environment scalability.</div><div><em>Conclusion:</em> There is a significant need to develop real-time emotion monitoring systems to empower caregivers in delivering timely, targeted interventions for individuals diagnosed with ASD. Future research should prioritise the development of real-time alert systems, autonomous intervention protocols, and solutions optimised for reliability, sustainability, usability, and adaptability across heterogeneous care settings.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106262"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979049","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":"When artificial intelligence guides and misguides clinicians: A critical appraisal of AI recommendation correctness and diagnostic decision-making","authors":"Hasan Nawaz Tahir ,&nbsp;Anfal Khan ,&nbsp;Muhammad Yousaf ,&nbsp;Shahnila Javed ,&nbsp;Muhammad Kamran Khan ,&nbsp;Yousaf Ali","doi":"10.1016/j.ijmedinf.2026.106293","DOIUrl":"10.1016/j.ijmedinf.2026.106293","url":null,"abstract":"","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106293"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146020816","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":"Large Language Models’ Performances regarding logical observation identifiers names and codes mapping in laboratory medicine: A comparative analysis of ChatGPT-4.0, Gemini, and Perplexity","authors":"Shinae Yu ,&nbsp;Eun-Jung Cho ,&nbsp;Sollip Kim ,&nbsp;Kuenyoul Park ,&nbsp;Min-Sun Kim ,&nbsp;YeJin Oh ,&nbsp;Hyejin Ryu","doi":"10.1016/j.ijmedinf.2026.106270","DOIUrl":"10.1016/j.ijmedinf.2026.106270","url":null,"abstract":"<div><h3>Objectives</h3><div>This study aimed to assess the feasibility and practical utility of using large language models (LLMs) for Logical Observation Identifiers Names and Codes (LOINC) mapping to standardise healthcare data in the field of laboratory medicine. We evaluated the accuracy and applicability of three LLMs—ChatGPT-4.0 (OpenAI), Gemini 1.5 (Google DeepMind), and Perplexity AI (<span><span>Perplexity.ai</span><svg><path></path></svg></span>)—in mapping laboratory test items, which typically require considerable institutional-level standardisation efforts.</div></div><div><h3>Methods</h3><div>A total of 75 representative laboratory test items, including 55 clinical chemistry and 20 hematology tests commonly used in clinical practice, were selected. Six board-certified clinical pathologists independently mapped each test item to its appropriate LOINC code. A consensus mapping was established by the experts and used as the gold standard. Each LLM’s output was compared to this consensus, and the results were categorised as complete match (CM), partial match (PM), or mismatch (MM) based on agreement with the reference.</div></div><div><h3>Results</h3><div>Overall paired ordinal analyses demonstrated a significant difference in LOINC code-mapping performance among the three models, with Gemini performing significantly worse than both ChatGPT-4.0 and Perplexity AI, and no significant difference between ChatGPT-4.0 and Perplexity AI. ChatGPT-4.0 achieved the highest CM rate in clinical chemistry (58.2%), whereas Perplexity AI performed best in hematology (55.0%). Gemini showed the highest MM rates, particularly in hematology (80.0%), while partial matches were largely attributable to method-related discrepancies rather than fully incorrect mappings.</div></div><div><h3>Conclusion</h3><div>Structured inputs, localisation to domestic laboratory practices, and expert oversight are critical to improving the reliability of LLM-generated LOINC mappings. While LLMs can reduce workload by generating candidate mappings, human validation remains essential to ensure clinical accuracy. Future improvements should focus on algorithmic refinement, error feedback integration, and adaptation to diverse laboratory settings to enhance accuracy and generalisability in real-world laboratory settings.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106270"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145935900","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 scoping review: how evaluation methods shape our understanding of ChatGPT’s effectiveness in healthcare","authors":"Yuanyuan Liu ,&nbsp;Yu Zhang,&nbsp;Haoran Mao","doi":"10.1016/j.ijmedinf.2025.106248","DOIUrl":"10.1016/j.ijmedinf.2025.106248","url":null,"abstract":"<div><h3>Background</h3><div>The rapid growth in research on ChatGPT’s healthcare applications has led to diverse evaluation methods and substantially heterogeneous findings, undermining evidence reliability and hindering clinical translation.</div></div><div><h3>Objectives</h3><div>This review aims to examine how different evaluation methods shape our understanding of ChatGPT’s effectiveness in healthcare.</div></div><div><h3>Methods</h3><div>Studies published between 2023 and 2024 that assess the use of ChatGPT in medical or healthcare-related contexts were included. Evidence was obtained from peer-reviewed literature analyzing ChatGPT’s applications across clinical, educational, and diagnostic domains. Following the PRISMA guidelines, this systematic review analyzed 131 studies published during 2023–2024 that assess the use of ChatGPT in medical contexts.</div></div><div><h3>Results</h3><div>The results indicate that predominant evaluation approaches—controlled trial studies, expert assessment studies, measurement-based evaluation studies, and prompt generation analysis studies—systematically influence conclusions about ChatGPT’s performance due to their inherent methodological characteristics, such as subjectivity, objectivity, and differences in ecological validity. Further analysis reveals that ChatGPT’s performance is highly context-dependent, shaped by specific application scenarios, model versions, and prompting strategies.</div></div><div><h3>Conclusions</h3><div>To address methodological heterogeneity and the lack of standardization, this study recommends multi-method cross-validation strategies and a risk-stratified, standardized evaluation framework. These steps are essential to enhance the scientific rigor and reliability of ChatGPT’s assessment in healthcare and to provide a solid foundation for its clinical integration.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106248"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886218","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":"Advantages and challenges of tracking st-segment elevation myocardial infarction patients with a real-time dashboard: A single-centre experience","authors":"Egidio de Mattia ,&nbsp;Filippo Paoletti ,&nbsp;Daniela Pedicino ,&nbsp;Giovanna Liuzzo ,&nbsp;Carmen Angioletti ,&nbsp;Alessia d’Aiello ,&nbsp;Alessio Perilli ,&nbsp;Andrea Adduci ,&nbsp;Giovanni Arcuri ,&nbsp;Emilio Meneschincheri ,&nbsp;Barbara Ruffo ,&nbsp;Melissa D’Agostino ,&nbsp;Rita De Donno ,&nbsp;Antonio Giulio de Belvis","doi":"10.1016/j.ijmedinf.2026.106261","DOIUrl":"10.1016/j.ijmedinf.2026.106261","url":null,"abstract":"<div><h3>Background</h3><div>Timely primary percutaneous coronary intervention (pPCI) is the most important treatment to improve outcomes in ST-segment elevation myocardial infarction (STEMI), with a strong relationship between treatment delays and morbidity and mortality. The present study aims to define the main steps for setting up a real-time digital monitoring dashboard to improve the clinical performance of STEMI management and to evaluate the impact of its implementation on the proportion of patients receiving primary percutaneous coronary intervention (pPCI) within 90 min.</div></div><div><h3>Methods</h3><div>The set-up of the digital monitoring system required the definition of detailed algorithms for the diagnosis, treatment, and rehab/follow-up phase. For each patient with a diagnosis of STEMI included in the clinical pathway (CP) a multidisciplinary working group identified i) rules for flagging patients alongside the CP, based on specific risk scores; ii) the critical points of the CP to be monitored, such as door-to-balloon time, intensive care unit length of stay, and total hospital length of stay. An interrupted time series analysis and multivariable logistic regression models were performed to assess for changes in the outcome (pPCI within 90 min) after the platform implementation, adjusting for temporal and individual confounders.</div></div><div><h3>Results</h3><div>After the introduction of the dashboard, the proportion of timely pPCI improved from 40 % pre-implementation to 65 % post-implementation. Adjusted models indicated a twofold increase in the odds of meeting the 90-minute benchmark (OR = 2.00; 95 % CI: 0.99–4.12).</div></div><div><h3>Conclusion</h3><div>The real-time monitoring system showed a positive impact on the timely management of STEMI, highlighting the potential for improving healthcare efficiency and patient outcomes.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106261"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979483","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":"Enhancing diabetes monitoring systems’ reports: A novel integrated diabetes report (IDR)","authors":"Tahmineh Aldaghi ,&nbsp;Robert Bem ,&nbsp;Jan Muzik","doi":"10.1016/j.ijmedinf.2026.106288","DOIUrl":"10.1016/j.ijmedinf.2026.106288","url":null,"abstract":"<div><h3>Aim</h3><div>Individuals with diabetes require continuous self-management. Diabetes monitoring systems generate structured reports that help individuals and healthcare providers interpret data and optimize treatment strategies. To design and validate an Integrated Diabetes Report (IDR) that improves the clarity, usability, and clinical relevance of diabetes data visualizations.</div></div><div><h3>Method</h3><div>A review of 13 diabetes monitoring systems revealed five main report categories: overlay, logbook, device-specific, daily, and overview reports. While the overview report was the most frequently used, it lacked comprehensive visualization and essential clinical metrics. To address these gaps, a multidisciplinary panel of four experts collaborated to design a more integrated reporting framework.</div></div><div><h3>Results</h3><div>Across systems, glucose statistics were included in all reports, followed by insulin data (in 12 systems), carbohydrate intake (in 6 systems), hypo-hyperglycemic indices (in 2 systems), sleep indices (in 2 systems), and medication details (in 1 system). Key gaps included minimal data on physical activity, limited documentation of carbohydrates, and the absence of consolidated insulin visualization. The IDR introduces a complications section, an integrated graph combining AGP with basal and bolus insulin, and an advanced insulin profile comparing seven calculated indices.</div></div><div><h3>Conclusion</h3><div>The IDR improves clinical interpretation, supports treatment decisions, and enhances risk assessment for diabetes management.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106288"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146013318","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 review of evaluation system for Internet hospitals","authors":"Zheqing Li ,&nbsp;Liyang Tang ,&nbsp;Yin Li ,&nbsp;Yuanyuan Dang ,&nbsp;Lin Yao","doi":"10.1016/j.ijmedinf.2025.106234","DOIUrl":"10.1016/j.ijmedinf.2025.106234","url":null,"abstract":"<div><h3>Context</h3><div>Internet hospitals have emerged as a digital innovation in healthcare, optimizing resource allocation and enhancing patient experience. They also support hierarchical diagnosis and treatment and contribute to the Healthy China initiative.</div></div><div><h3>Objectives</h3><div>To establish a comprehensive evaluation system to promote the sustainable development of Internet hospitals.</div></div><div><h3>Methods</h3><div>A systematic review of literature related to the evaluation of Internet-based healthcare services was conducted. Using Web of Science and CNKI as data sources, studies published between 2015 and 2024 were screened based on predefined criteria, focusing on high-quality journals and research reports. The selected literature was coded and analyzed across four dimensions: patient services, doctor services, management services, and information security.</div></div><div><h3>Results</h3><div>The final analysis included 34 papers, with 25 mentioning patient services indicators, 20 mentioning doctor services indicators, 18 mentioning medical services process management indicators, and 9 mentioning information security. This study identifies key evaluation indicators and examines their interrelationships, highlighting potential systemic risks from localized optimizations.</div></div><div><h3>Conclusion</h3><div>This review analyzed Internet hospital evaluation across patient services, doctor services, services management, and information security. While it highlights potential efficiency gains, it notes the lack of comprehensive indicators, limiting assessment and improvement. For sustainable development, a more comprehensive evaluation system should integrate multi-stakeholder perspectives (patients, doctors, institutions), address systemic risks from localized optimization, and incorporate coordinated policy considerations.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106234"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145913911","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":"Automated extraction of fluoropyrimidine treatment and treatment-related toxicities from clinical notes using natural language processing","authors":"Xizhi Wu ,&nbsp;Madeline S. Kreider ,&nbsp;Philip E. Empey ,&nbsp;Chenyu Li ,&nbsp;Yanshan Wang","doi":"10.1016/j.ijmedinf.2026.106276","DOIUrl":"10.1016/j.ijmedinf.2026.106276","url":null,"abstract":"<div><h3>Objective</h3><div>Fluoropyrimidines are widely prescribed for colorectal and breast cancers, but are associated with toxicities such as hand-foot syndrome and cardiotoxicity. Since toxicity documentation is often embedded in clinical notes, we aimed to develop and evaluate natural language processing (NLP) methods to extract treatment and toxicity information.</div></div><div><h3>Materials and methods</h3><div>We constructed a gold-standard dataset of 236 clinical notes from 204,165 adult oncology patients. Domain experts annotated categories related to treatment regimens and toxicities. We developed rule-based, machine learning-based (Random Forest [RF], Support Vector Machine [SVM], Logistic Regression [LR]), deep learning-based (BERT, ClinicalBERT), and large language models (LLM)-based NLP approaches (zero-shot and error analysis prompting). A 5-fold cross validation were conducted to validate each model.</div></div><div><h3>Results</h3><div>Error analysis prompting achieved optimal precision, recall, and F1 scores for treatment (F1 = 1.000) and toxicities extraction (F1 = 0.965), whereas zero-shot perform moderately (treatment F1 = 0.889, toxicities extraction F1 = 0.854) Rule-based reached F1 = 1.000 for treatment and F1 = 0.904 for toxicities extraction. LR and SVM ranked second and fourth for toxicities extraction (LR F1 = 0.914, SVM F1 = 0.903). Deep learning and RF underperformed, with performance of BERT reached F1 = 0.792 for treatment and F1 = 0.837 for toxicities extraction.,ClinicalBERT reached F1 = 0.797 for treatment and F1 = 0.884 for toxicities extraction). RF reached F1 = 0.745 for treatment and F1 = 0.853 for toxicities extraction.</div></div><div><h3>Discussion</h3><div>LMM-based error analysis outperformed all others, followed by machine learning methods. Machine learning and deep learning methods were limited by small training data and showed limited generalizability, particularly for rare categories.</div></div><div><h3>Conclusion</h3><div>LLM-based error analysis most effectively extracted fluoropyrimidine treatment and toxicity information from clinical notes, and has strong potential to support oncology research and pharmacovigilance.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106276"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979481","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}