International Journal of Medical Informatics最新文献

{"title":"Development and validation of an Interpretable Machine learning model for Discriminating between benign and malignant breast cancer","authors":"Zhichun Wang ,&nbsp;Weixiang Liu ,&nbsp;Lin Hua ,&nbsp;Xiang Li ,&nbsp;Guohui Xue","doi":"10.1016/j.ijmedinf.2026.106300","DOIUrl":"10.1016/j.ijmedinf.2026.106300","url":null,"abstract":"<div><h3>Objective</h3><div>Breast cancer prognosis depends on early detection. We developed and externally validated a model using routine, readily available clinical and laboratory variables to discriminate malignant from benign breast lesions, aiming to reduce unnecessary biopsies and support early decision-making.</div></div><div><h3>Methods</h3><div>This retrospective two-center study included a development cohort 1from Jiujiang First People’s Hospital (N = 745; malignant 573, benign 172) and an external cohort2 from the First Affiliated Hospital of Nanchang University (N = 221; malignant 161, benign 60).Cohort 1 was randomly split into a 70:30 training and test set. Five-fold cross-validation was used to compare multiple algorithms and lock the model and hyperparameters; the locked model was evaluated on a fixed test set and the external cohort. The primary metric was AUC, with sensitivity, specificity, F1, Brier score, calibration curve, decision curve analysis (DCA), and SHAP for explanation.</div></div><div><h3>Results</h3><div>Logistic regression was selected, using Age, TT, APTT, CEA, and Ca. Cross-validated AUCs were 0.910 (training) and 0.905 (internal validation). The fixed test set yielded AUC 0.865 (sensitivity 0.802; specificity 0.712; F1 0.849; Brier 0.112). External validation achieved AUC 0.861, specificity 0.883, and PPV 0.934. DCA showed net benefit over “treat-all/none” across 20 %–95 % threshold probabilities. SHAP identified Age, TT, CEA, APTT and Ca as the dominant contributors.</div></div><div><h3>Conclusions</h3><div>A logistic model based on routine laboratory variables effectively distinguishes malignant from benign breast lesions, with robust external performance and clear clinical net benefit, enabling early risk stratification and fewer unnecessary biopsies.This study proposes a tool that quantifies breast tumor malignancy risk using only objective indicators, without subjective factors. Online tool: <span><span>prediction-for-bc.shinyapps.io/dynnomapp/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"210 ","pages":"Article 106300"},"PeriodicalIF":4.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146067914","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":"Case-based reasoning for clinical trial recruitment tools in oncology: When you need patients to find patients","authors":"Lou-Anne Guillotel ,&nbsp;Thierry Lesimple ,&nbsp;Oussama Zekri ,&nbsp;Marc Cuggia ,&nbsp;Boris Campillo-Gimenez","doi":"10.1016/j.ijmedinf.2026.106301","DOIUrl":"10.1016/j.ijmedinf.2026.106301","url":null,"abstract":"<div><h3>Background</h3><div>Patient recruitment for clinical trials remains a major challenge, with 86% of trials failing to meet enrollment targets on time. In over 77% of cases, recruitment difficulties stem from matching problems between trials and patients. Case-Based Reasoning (CBR) offers a distinct patient-to-patient approach by determining eligibility through comparison with previously enrolled patients, yet this methodology remains underexplored in contemporary oncology trial matching despite its potential advantages.</div></div><div><h3>Objective</h3><div>To compare the performance of two CBR approaches—random forest (RF) and target patient similarity (TPS)—in predicting patient eligibility for recent oncology clinical trials using real-world electronic health record data.</div></div><div><h3>Methods</h3><div>We selected three breast cancer clinical trials (2019–2022) from our institutional registry. Patient data were extracted from our clinical data warehouse, including structured data (laboratory results, diagnosis codes, procedures, treatments) and unstructured clinical narratives processed using natural language processing. For each trial, we trained RF classifiers and TPS models using repeated hold-out validation (25 splits, 70/30 train-test). Performance was evaluated using discriminative metrics (AUC, positive precision, recall, F1-score) and ranking metrics (P@5, P@10, MAP, MRR, NDCG@5, NDCG@10). We analyzed model performance across varying numbers of eligible patients in training datasets (2 to 70% of the total number of eligible patients).</div></div><div><h3>Results</h3><div>Both approaches demonstrated strong discriminative performance across three trials, with average AUCs of 84.1 % for RF and 76.4 % for TPS, driven primarily by high recall (82.3 % and 77.7 %, respectively). However, positive precision remained low (13.3 % and 9.9 %), reflecting high false-positive rates due to class imbalance. RF showed superior ranking performance, particularly for the trial with the largest eligible cohort (n = 542; P@5 = 78.6 %, MRR = 88.0 %), compared to TPS (P@5 = 47.9 %, MRR = 69.2 %). Both approaches reached performance plateaus with only around 10 eligible patients in training datasets. Variable importance analysis revealed that treatment-related features, diagnostic codes, and procedures were consistently the most important predictors, with relevant patterns identified even with minimal training data.</div></div><div><h3>Conclusions</h3><div>CBR approaches can effectively support patient pre-screening for oncology clinical trials, with RF demonstrating moderately superior performance over TPS. Both methods show robust discriminative performance with small training datasets, though ranking performance varies substantially across trials. Our findings suggest that CBR approaches may benefit from integration with query-based or prompt-based methods during early recruitment phases when training data is scarce.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106301"},"PeriodicalIF":4.1,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146137631","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":"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":"2026-01-18","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":"A structured decision-support framework for selecting imputation methods in clinical structured datasets: A secondary analysis","authors":"Marziyeh Afkanpour ,&nbsp;Mehri Momeni ,&nbsp;Hamed Tabesh","doi":"10.1016/j.ijmedinf.2026.106298","DOIUrl":"10.1016/j.ijmedinf.2026.106298","url":null,"abstract":"<div><h3>Objective</h3><div>Missing values are a common challenge in healthcare data analysis, and inadequate handling can introduce bias and undermine the validity of findings. Imputation methods offer a practical solution, but selecting an appropriate approach depends on multiple dataset-specific factors. This study proposes a structured decision-support framework that defines key prerequisites for choosing suitable imputation methods during the preprocessing of clinically structured datasets.</div></div><div><h3>Methods</h3><div>A secondary analysis of a previous systematic review was conducted, covering 69 studies to identify factors influencing imputation method selection. Domain experts evaluated assumptions regarding missing data characteristics and dataset structure, reaching consensus on the most relevant factors. These factors were synthesized into a structured framework designed to guide systematic and transparent imputation method selection in clinical data preprocessing workflows.</div></div><div><h3>Results</h3><div>Nine key factors were identified as essential for determining an appropriate imputation method. These include missing data characteristics, mechanism, pattern, and ratio and dataset attributes such as data type, variable role, distribution, and correlation. The ratio of missingness was the most influential factor, followed by variable role and missing value mechanism. Most studies emphasized the combined importance of both missing data properties and dataset features in imputation selection.</div></div><div><h3>Conclusions</h3><div>Understanding the characteristics of missing values and dataset structure is crucial for selecting appropriate imputation methods. The proposed structured decision-support framework provides an evidence-based checklist to enhance transparency, reproducibility, and reliability in preprocessing clinical datasets within medical informatics workflows.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"210 ","pages":"Article 106298"},"PeriodicalIF":4.1,"publicationDate":"2026-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081164","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":"Clinician preferences for explainable AI in critical care: a comparative study of interpretable models and visualizations for intubation decision support","authors":"Tiantian Xian ,&nbsp;Nikolay Mehandjiev ,&nbsp;Panos Constantinides ,&nbsp;Yu-wang Chen ,&nbsp;Qudamah Quboa ,&nbsp;Gareth Kitchen","doi":"10.1016/j.ijmedinf.2026.106287","DOIUrl":"10.1016/j.ijmedinf.2026.106287","url":null,"abstract":"<div><h3>Background:</h3><div>The complexity of many AI models hinders their clinical adoption because the clinicians using them do not regard them as transparent. This study addresses the lack of clinician-centered explainable AI (XAI) interfaces by designing and evaluating intuitive visual explanations for intubation prediction, testing the hypothesis that workflow-compatible designs enhance acceptance.</div></div><div><h3>Objective:</h3><div>This study compares three, time-aware, visual explanations for XAI-based intubation prediction and evaluate their acceptance, comprehension, and perceived utility among clinicians.</div></div><div><h3>Methods:</h3><div>We developed machine learning models to estimate the near-term risk of deterioration in the patient’s condition which may lead to mechanical intubation using ICU time-series data. We generated global and local explanations using SHAP and designed three customized visual formats—a temporal force plot, a temporal bar chart, and a dual-encoded SHAP heatmap. Clinicians (<em>n</em> = 206) evaluated comprehension and usability using objective questions and a Likert-based survey.</div></div><div><h3>Results:</h3><div>Based on 4608 critically ill patients with 10 medical variables over 7 hours of data for each patient, the Random Forest (RF) model achieved the highest area under the curve (AUC): 0.94. Furthermore, the local explanations were customized and evaluated by 206 clinicians through a survey conducted on the Prolific platform. A customized heatmap representation was selected as the visualization with the highest perceived clinical utility and alignment with clinical workflows.</div></div><div><h3>Discussion:</h3><div>The reported findings support the need for explanation formats to be tailored to clinical reasoning and task context, supporting the concept of cognitive fit. The heatmap’s close alignment with clinicians’ mental models and its graphical integrity enhances interpretability and trust. This study demonstrates that explanation effectiveness depends on contextual relevance, rather than a universal standard, and that the presentation format itself significantly shapes clinicians’ trust in XAI systems.</div></div><div><h3>Conclusion:</h3><div>This study advances clinical XAI by introducing a time-aware explanation framework for ICU intubation decisions. By integrating temporal trends with model reasoning, our visualizations closely align with clinicians’ cognitive workflows. Rigorous clinician-centered evaluation identified the dual-encoded SHAP heatmap as the most useful and workflow-compatible visualization, highlighting the importance of explanation design alongside predictive accuracy for clinical adoption.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"210 ","pages":"Article 106287"},"PeriodicalIF":4.1,"publicationDate":"2026-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146006657","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":"Rule-augmented constraint learning for semantic error detection in MIMIC-III knowledge graph","authors":"Özge Noben,&nbsp;Ömer Durukan Kılıç,&nbsp;Tjitze Rienstra,&nbsp;Michel Dumontier,&nbsp;Remzi Celebi","doi":"10.1016/j.ijmedinf.2026.106297","DOIUrl":"10.1016/j.ijmedinf.2026.106297","url":null,"abstract":"<div><div>High-quality, error-free data is essential for developing reliable data-driven models, particularly in clinical decision support systems where inaccurate predictions can have serious consequences. While KGs offer a structured and semantically rich representation for clinical data, ensuring their consistency and correctness remains a challenge. Existing rule mining techniques provide solutions for the automatic extraction of logical constraints from KGs, but they often produce redundant or clinically irrelevant rules, especially when dealing with numeric or categorical literals such as age or lab values. KG constraints—rules intended to capture implausible or conflicting facts in the KG—can be used to spot semantic errors: facts that might conform to the underlying schema but contradict domain knowledge. In this work, we propose a novel framework for constraint learning in clinical KGs that identifies and transforms high-confidence rules into clinically plausible constraints. We propose two approaches, based on class disjointness and literal clustering combined with rule mining. We validate the clinical relevance of these generated rules using expert-curated constraints and large language models (LLMs). The results on the MIMIC-III clinical dataset show that rule filtering based constraint learning effectively preserves clinically meaningful rules that align with established medical knowledge. For numeric data, we achieve reliable value groupings through our clustering-based method, and the rules derived from these groupings were validated by LLMs. Their outputs confirm the clinical relevance of a portion of those discovered rules. By providing interpretable and scalable solutions to semantic inconsistencies in KGs, this study contributes to increasing the KG trustworthiness and its clinical usability.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"210 ","pages":"Article 106297"},"PeriodicalIF":4.1,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146006656","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":"2026-01-17","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":"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":"2026-01-17","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":"Less time Coding, more time Caring: Performance evaluation of ChatGPT-5 for ICD-10 coding of radiology reports","authors":"Tristan Ruhwedel ,&nbsp;Julian M.M. Rogasch ,&nbsp;Paul Martin Dahlke ,&nbsp;Seyd Shnayien ,&nbsp;Christian Furth ,&nbsp;Christoph Wetz ,&nbsp;Holger Amthauer ,&nbsp;Imke Schatka ,&nbsp;Nick Lasse Beetz","doi":"10.1016/j.ijmedinf.2026.106296","DOIUrl":"10.1016/j.ijmedinf.2026.106296","url":null,"abstract":"<div><h3>Introduction</h3><div>Worldwide radiologists are facing a high administrative workload. ICD-10 coding is mandatory for reimbursement in many health systems and a frequent source of billing errors. Large language models have shown promise in supporting coding related tasks, but previous studies with earlier ChatGPT versions reported mixed results and evidence specific to radiology reports remains scarce. We therefore aimed to investigate whether ChatGPT-5 can be consulted when assigning ICD-10 codes to radiology reports and whether this leads to a measurable time advantage.</div></div><div><h3>Methods</h3><div>2,738 fictious radiology reports across multiple modalities were derived from the PARROT database. Additionally, 100 fictitious PET/CT reports were created. Each report was assigned a single, most relevant ICD-10 code using ChatGPT-5. For PARROT, ChatGPT-derived codes were compared with predefined database reference labels. For PET/CT, ChatGPT-derived codes were compared with codes assigned by an independent manual coder. Exact and character-level concordance were assessed. In cases of discordance, a blinded adjudicator selected the most accurate ICD-10 code. Coding efficiency was evaluated for PET/CT reports by measuring coding time per report.</div></div><div><h3>Results</h3><div>For PARROT, exact-code concordance was 1,590/2,738 (58.1 %). In a random subset of 200 mismatches, blinded adjudication preferred the ChatGPT derived code in 123 and the reference label in 77 cases (p = 0.0015). Coding non-English reports resulted in significantly lower concordance (first character: p = 0.002; second/third characters: p &lt; 0.001; last characters: p = 0.012) and longer coding times than English reports (p = 0.002). Regarding PET/CT reports, median coding time was 8 s with ChatGPT and 135 s without. The median time saved was 127 s per report.</div></div><div><h3>Conclusion</h3><div>Applied to daily clinical care, higher code correctness might reduce billing errors, while saved time could be reallocated to patient care. Radiologists should collaborate with developers to create versions of LLMs that operate within data-secure environments.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"210 ","pages":"Article 106296"},"PeriodicalIF":4.1,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026173","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":"Biometric Data in Post-Traumatic Stress Disorder Detection: A Scoping Review of Digital Health Applications","authors":"Phue Thet Khaing,&nbsp;Masaharu Nakayama","doi":"10.1016/j.ijmedinf.2026.106289","DOIUrl":"10.1016/j.ijmedinf.2026.106289","url":null,"abstract":"<div><h3>Context</h3><div>Post-traumatic stress disorder (PTSD) is mainly assessed through self-reports and clinician interviews, which can delay recognition and limit reach. Biometric markers captured using digital technologies may enable earlier and more objective detections.</div></div><div><h3>Purpose</h3><div>To map biometric modalities used for PTSD detection in digital health, identify underused markers, characterise machine learning (ML)/artificial intelligence (AI) approaches, and assess sex-related analyses.</div></div><div><h3>Methods</h3><div>Guided by PRISMA-ScR, a protocol on the Open Science Framework was pre-registered and searches in PubMed, IEEE Xplore, and Google Scholar (2015–2025) were conducted. The full search string was: (“post-traumatic stress disorder” OR “PTSD”) AND (“biometric data” OR “biosensor” OR “wearable technology”) AND (“detection” OR “screening” OR “diagnosis” OR “monitoring”) AND (“digital health” OR “mobile health” OR “AI-based” OR “machine learning”). Peer-reviewed human studies using biometric data with digital tools and/or ML/AI for PTSD detection were eligible. Of 3,312 records, 89 underwent full-text review, and 18 studies met the inclusion criteria.</div></div><div><h3>Analysis</h3><div>Data were categorised by biometric modality, digital platform (wearable devices, mobile applications, ML/AI systems), study population, and performance metrics (area under the curve, sensitivity, specificity). Findings were grouped thematically (physiological, neuroimaging, behavioural, genetic, multimodal) and synthesised narratively to identify trends, gaps, and the application of sex-stratified modelling.</div></div><div><h3>Results</h3><div>Most studies focused on physiological (e.g., heart rate, sleep) and neuroimaging (functional magnetic resonance imaging, electroencephalography) signals; behavioural and genetic modalities were underexplored. Data were frequently captured via wearables and mobile platforms, with ML commonly applied. Performance reporting was uneven, sex-stratified analyses were rare, and several promising modalities (e.g., eye-tracking, electrodermal activity) remain underused.</div></div><div><h3>Conclusion</h3><div>Digital biometric approaches can detect PTSD; however, progress has been slowed by heterogeneous study designs, inconsistent reporting, and limited attention to sex differences. Establishing common reporting standards, evaluating multimodal models in real-world settings, and developing algorithms incorporating sex for more equitable screening are warranted.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106289"},"PeriodicalIF":4.1,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146115094","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}