International Journal of Medical Informatics最新文献

{"title":"Large language models as second reviewers for medical errors in real-world internal medicine reports: a prospective comparative study of open- and closed-source models","authors":"Roko Skrabic ,&nbsp;Ivan Viculin ,&nbsp;Zvonimir Boban ,&nbsp;Marko Kumric ,&nbsp;Marino Vilovic ,&nbsp;Josip Vrdoljak ,&nbsp;Josko Bozic","doi":"10.1016/j.ijmedinf.2026.106316","DOIUrl":"10.1016/j.ijmedinf.2026.106316","url":null,"abstract":"<div><h3>Objective</h3><div>Preventable errors in clinical documentation and decision-making remain a major threat to patient safety, yet the role of open-source large language models (LLMs) as practical “second reviewers” in general Internal Medicine remains unclear.</div></div><div><h3>Methods</h3><div>We prospectively assembled 102 real-world Emergency Internal Medicine reports (de-identified) and either inserted or confirmed realistic errors across four categories (diagnostics/investigations, medication/therapy, process/communication/follow-up, other). Three LLMs (open-source Deepseek-v3-r1 and GPT-OSS-120b, and closed-source OpenAI-o3) were prompted with a uniform system instruction to (i) localize the predefined error and (ii) recommend corrections. Two blinded Internal Medicine specialists independently graded outputs for error localization (0–1) and recommendation quality (Likert 1–4); disagreements were resolved analytically, and analyses used the more conservative rater. Three human clinicians independently reviewed subsets of the same cases to provide a comparator.</div></div><div><h3>Results</h3><div>Using the conservative rater, correct error localization was 72.5% (74/102; 95% CI 63.2–80.3) for Deepseek-v3-r1, 79.2% (80/101; 95% CI 70.3–86.0) for o3, and 65.7% (67/102; 95% CI 56.1–74.2) for GPT-OSS-120b (Cochran’s Q p = 0.033). Pairwise McNemar tests favored o3 over GPT-OSS-120b (p = 0.020; Holm-adjusted p = 0.060); other contrasts were not significant. Recommendation quality was high for all models (median 4/4), with mean ± SD scores of 3.73 ± 0.49 for Deepseek-v3-r1, 3.65 ± 0.64 for o3, and 3.51 ± 0.73 for GPT-OSS-120b. Inter-rater agreement was excellent for GPT-OSS-120b (κ = 0.94 for detection; κ_w = 0.85 for quality), substantial for Deepseek-v3-r1 (κ = 0.75; κ_w = 0.47), and lower for o3 (κ = 0.31; κ_w = 0.14). All models frequently flagged additional clinically useful issues (≥99% of reports).</div></div><div><h3>Conclusion</h3><div>In real-world Internal Medicine reports with realistic, expert-defined errors, state-of-the-art open-source LLMs approached the performance of a leading closed model and clearly outperformed clinicians in error detection, while providing predominantly guideline-concordant corrective recommendations. Given their advantages for privacy, customizability, and potential local deployment, open models represent credible candidates for privacy-preserving “second-reviewer” support in Internal Medicine. Prospective, workflow-embedded trials that also quantify specificity on error-free notes, alert burden, and patient outcomes are now warranted.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106316"},"PeriodicalIF":4.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146144750","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":"Explainable AI in Cardiology Diagnostics: A Systematic Review of Machine Learning, Meta-heuristic Optimization, and Clinical Text Mining for Coronary Artery Disease","authors":"Majdi Jaradat ,&nbsp;Mohammed Awad","doi":"10.1016/j.ijmedinf.2026.106321","DOIUrl":"10.1016/j.ijmedinf.2026.106321","url":null,"abstract":"<div><h3>Background</h3><div>This systematic review compiles evidence and examines how various artificial intelligence (AI) approaches, including machine learning (ML), natural language processing (NLP), <em>meta</em>-heuristic optimization, and explainable AI (XAI), are utilized to predict and diagnose coronary artery disease (CAD). We aim to identify the most commonly used models, evaluate their performance, and explore how interpretability and optimization enhance their usefulness in clinical practice.</div></div><div><h3>Method</h3><div>A thorough search was conducted across five major databases (PubMed, Scopus, IEEE Xplore, ACM Digital Library, and SpringerLink) to identify relevant studies published between January 2022 and August 2025, in accordance with the PRISMA guidelines. Dual independent reviewers performed study selection and data extraction. The quality of the included studies was evaluated using a checklist based on QUADAS-2. Data were collected on study characteristics, model types, validation methods, and performance metrics, which will be the cornerstone of the analysis.</div></div><div><h3>Results</h3><div>Sixty-one studies met the inclusion criteria. ML and deep learning models demonstrated strong performance and achieved high accuracy in benchmark datasets, but showed limited clinical validation. Transformer-based models (e.g., BioBERT, ClinicalBERT) showed high efficacy for medical text analysis, but require substantial data and computational resources. Meta-heuristic algorithms (e.g., Genetic Algorithms, Particle Swarm Optimization) effectively improved model efficiency but were rarely applied to unstructured clinical narratives. XAI tools (e.g., SHAP, LIME) improved model transparency, though most studies highlight a need for more rigorous evaluation.</div></div><div><h3>Conclusion</h3><div>Integrated ML, NLP, <em>meta</em>-heuristic optimization, and XAI hold significant promise in advancing the diagnosis of CAD by improving both accuracy and interpretability. However, challenges such as data scarcity, limited external validation, and a lack of standardized, clinician-centric explainability impede clinical adoption. Future research should focus on hybrid frameworks validated for large, diverse, and real-world datasets.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106321"},"PeriodicalIF":4.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146137980","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":"The effect of artificial intelligence–assisted pulmonary rehabilitation on exercise capacity: A systematic review and meta-analysis","authors":"Ecran Cinkavuk,&nbsp;Ebru Calik,&nbsp;Naciye Vardar-Yagli","doi":"10.1016/j.ijmedinf.2026.106336","DOIUrl":"10.1016/j.ijmedinf.2026.106336","url":null,"abstract":"<div><h3>Introduction</h3><div>Artificial intelligence (AI) technologies are increasingly being integrated into pulmonary rehabilitation (PR) to improve individualization, real-time monitoring, and adherence in individuals with chronic respiratory diseases. However, their clinical impact on exercise capacity remains unclear. This systematic review and <em>meta</em>-analysis aimed to evaluate the effectiveness of AI-supported PR programs compared to usual care in improving exercise capacity and respiratory function in adults with chronic respiratory diseases.</div></div><div><h3>Methods</h3><div>This systematic review and <em>meta</em>-analysis followed PRISMA guidelines and was registered with PROSPERO (ID: CRD420251075622). A comprehensive search was conducted across five electronic databases (PubMed, Web of Science, Scopus, Cochrane Central Register of Controlled Trials (CENTRAL) and PEDro) from inception to July 2025. Statistical analyses for the <em>meta</em>-analysis were conducted using RevMan 5.4.</div></div><div><h3>Results</h3><div>Three eligible RCTs with a total of 456 participants were included. Pooled analysis showed a significant improvement in 6-minute walk distance (6MWD) after AI-assisted PR group compared to control (MD: 22.08 m; 95% CI: 4.96–39.20; p = 0.01). Moderate heterogeneity was observed (I² = 40%). No <em>meta</em>-analysis was conducted for respiratory function due to insufficient pre-post data. Risk of bias was generally low, though participant blinding was absent in all studies. Methodological quality was good, with a mean PEDro score of 6.0 ± 0.82.</div></div><div><h3>Conclusion</h3><div>AI-supported PR can significantly improve exercise capacity in individuals with chronic respiratory diseases. Despite promising results, high-quality studies in different pulmonary patient groups are needed to address existing limitations, particularly regarding standardization, cost-effectiveness, and clinical integration of AI-technology.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106336"},"PeriodicalIF":4.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146144685","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 temporal validation of machine learning models for predicting clinically relevant medication reconciliation discrepancies at the emergency department: A single-center retrospective study","authors":"Greet Van De Sijpe ,&nbsp;Tuur Schrooten ,&nbsp;Sabrina De Winter ,&nbsp;Lorenz Van der Linden ,&nbsp;Peter Vanbrabant ,&nbsp;Annabel Dompas ,&nbsp;Bo Bertels ,&nbsp;Maarten De Vos ,&nbsp;Isabel Spriet","doi":"10.1016/j.ijmedinf.2026.106309","DOIUrl":"10.1016/j.ijmedinf.2026.106309","url":null,"abstract":"<div><h3>Objective</h3><div>Medication discrepancies at hospital admission are common and can cause preventable patient harm. Predictive models can help prioritize medication reconciliation for high-risk patients. This study aimed to develop and validate machine learning (ML) models for predicting clinically relevant medication reconciliation discrepancies in emergency department (ED) patients, and to compare their performance with logistic regression.</div></div><div><h3>Methods</h3><div>We conducted a single-center, retrospective study at UZ Leuven. The dataset included patients admitted to the ED between 2017 and 2019 (development set) and 2021–2022 (temporal validation set). The outcome variable was the presence of at least one clinically relevant medication discrepancy, defined by expert panel adjudication. Variables were extracted from the electronic health record, with care to avoid data leakage. Three models – logistic regression, random forest, and eXtreme Gradient Boosting – were developed using tailored variable selection strategies, and validated temporally. Model performance was assessed via discrimination, calibration, and classification metrics. Clinical utility was assessed using decision curve analysis.</div></div><div><h3>Results</h3><div>The development and validation cohorts included 817 and 349 patients, respectively. LR and RF models demonstrated moderate discrimination on temporal validation (AUROC 0.67–0.68). The XGBoost model showed lower discrimination (AUROC 0.63). Calibration was comparable across models. Decision curve analysis showed only small differences in net benefit between models across clinically relevant threshold probabilities.</div></div><div><h3>Conclusion</h3><div>ML models provided no clear improvement over logistic regression, which achieved similar predictive performance and greater interpretability. These findings highlight both the potential and the limitations of ML for supporting targeted medication reconciliation in ED workflows. Future research should explore the added value of richer data sources, such as unstructured clinical narratives.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106309"},"PeriodicalIF":4.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146108314","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":"Applying a statistical model-based AI method to identify prognostic factors for long-term cognitive decline in Alzheimer’s disease: Evidence from pooled placebo data of four phase III trials","authors":"Ryoichi Hanazawa ,&nbsp;Hiroyuki Sato ,&nbsp;Keisuke Suzuki ,&nbsp;Akihiro Hirakawa","doi":"10.1016/j.ijmedinf.2026.106337","DOIUrl":"10.1016/j.ijmedinf.2026.106337","url":null,"abstract":"<div><h3>Background</h3><div>Heterogeneity in the long-term progression of Alzheimer’s disease (AD) challenges the efficiency of clinical trials. Identifying long-term prognostic factors is critical for enhancing trial efficiency, although it has been limited by the lack of appropriate statistical approaches. We applied a recently developed statistical model-based AI method to identify the baseline prognostic factors for long-term cognitive decline in a clinical trial population.</div></div><div><h3>Methods</h3><div>We analyzed pooled placebo arm data (N = 1,597) from four Phase III trials in patients with mild-to-moderate AD. Long-term trajectories for the Mini-Mental State Examination (MMSE), 11- and 14-item versions of the Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog11, ADAS-Cog14), and Clinical Dementia Rating-Sum of Boxes (CDR-SB) were predicted from their short-term data (≤80 weeks). Trajectories were compared between subgroups defined by six baseline factors (age, sex, <em>apolipoprotein E ε4</em> [<em>APOE ε4</em>] status, years of education, years from diagnosis, and years from disease onset) using the area under the curve (AUC).</div></div><div><h3>Results</h3><div>Longer years of education (≥13 years) was the most robust predictor associated with faster progression across all four outcomes (e.g., for 20-year ADAS-Cog11, AUC ratio, 1.11, p &lt; 0.001). Younger age (&lt;74 years) was associated with a faster decline in MMSE and ADAS-Cog scores, but not in CDR-SB. <em>APOE ε4</em> status, sex, years from diagnosis, and years from disease onset were not significantly associated with long-term progression.</div></div><div><h3>Conclusions</h3><div>Baseline educational level and age were significant prognostic factors of long-term cognitive decline. These findings will help optimize patient stratification in future clinical trials on AD.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106337"},"PeriodicalIF":4.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190933","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":"Machine learning models for identifying urinary incontinence in women with a history of hysterectomy using basic demographic and clinical characteristics: A cross-sectional study","authors":"Lu Liu ,&nbsp;Wei Chen ,&nbsp;Lili Li ,&nbsp;Ping Zhang","doi":"10.1016/j.ijmedinf.2026.106334","DOIUrl":"10.1016/j.ijmedinf.2026.106334","url":null,"abstract":"<div><h3>Background</h3><div>Urinary incontinence (UI) in women with a history of hysterectomy represents a significant global health concern. It is crucial to clarify the association between hysterectomy for benign indications and UI to avoid unnecessary surgery.</div></div><div><h3>Objective</h3><div>This study aimed to develop a machine learning (ML) model to identify factors associated with UI in women with a history of hysterectomy.</div></div><div><h3>Methods</h3><div>We analyzed 2021 patients from the National Health and Nutrition Examination Survey (NHANES) database who underwent hysterectomy for benign indications as our derivation cohort. Thirteen demographic and clinical features were evaluated: age, educational, anthropometric measurements (height, weight, waist), medical history diabetes mellitus (DM), and reproductive history. Six ML algorithms were employed: logistic regression (LR), naïve Bayes (NB), multilayer perceptron (MLP), extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM). External validation was performed on a cohort consisting of 556 patients from the Second Qilu Hospital of Shandong University. To improve interpretability, the predictive process was graphically illustrated employing a nomogram and SHapley Additive exPlanations (SHAP). Finally, the model was deployed as an online clinical decision support platform for applications.</div></div><div><h3>Results</h3><div> <!-->A comparison of receiver operating characteristic (ROC) curves using LR as the reference model revealed no statistically significant differences across the six ML algorithms. In the internal validation cohorts, the models achieved area-under-the-curve (AUC) values of 0.753–0.763 and accuracies between 0.627 and 0.664. This predictive performance was sustained in the external-validation cohort, with AUC values ranging from 0.702 to 0.718 and accuracies ranging from 0.661 to 0.697.</div></div><div><h3>Conclusion</h3><div> <!-->Our findings demonstrated that ML models could effectively identify UI in women with a history of hysterectomy. This approach, facilitated by the nomogram and online tool, enhanced the feasibility and accessibility of identifying women at risk.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106334"},"PeriodicalIF":4.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146167897","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 systematic review of the causes of morbidity data quality issues","authors":"Sam Yan ,&nbsp;Jessica Dickson ,&nbsp;Brandon Cheong ,&nbsp;Heather Grain ,&nbsp;John Oldroyd","doi":"10.1016/j.ijmedinf.2026.106333","DOIUrl":"10.1016/j.ijmedinf.2026.106333","url":null,"abstract":"<div><h3>Background</h3><div>The quality of hospital morbidity data collected with the International Classification of Diseases is unknown. A systematic review of the causes of morbidity data quality issues is urgently needed.</div></div><div><h3>Objectives</h3><div>We aimed to systematically identify and investigate the root causes of issues associated with hospital morbidity data collected using the International Classification of Diseases 10th edition, Australian Modification (ICD-10-AM) and Australian Classification of Health Interventions (ACHI).</div></div><div><h3>Methods</h3><div>This review included studies related to morbidity data collection issues arising from using ICD-10-AM and ACHI from Scopus, Embase, Medline and other data sources from 2017 to January 2025 in English. The quality of included studies was assessed using SQUIRE and STROBE checklists. A narrative synthesis was undertaken with themes and sub-categories of issues identified. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 Statement.</div></div><div><h3>Results</h3><div>Fifty-two studies were included, 37 from Australia, 3 from Canada, 2 each from Ireland and New Zealand, and 1 each from France, Germany, Turkey, US. Four themes were identified: 1) quality issues in standards, 2) technology, 3) education and training, and 4) issues related to clinical practice. There exists ambiguity in standards due to optional guidelines in data processing and jurisdictional differences. The standards do not provide sufficient granularity for precise disease identification. The standards are not capable of linking complex diagnostic, causal and procedural relationships and are leading to technical and other categories of issues. The complexity of issues associated with the standard leads to insufficient training resources for staff worldwide. Fragmented information structure and changes in clinical documentation rules lead to inconsistent coding.</div><div>Interpretation.</div><div>The root causes of the morbidity data collection errors are mainly associated with the quality of the standards. Further research is needed to address the root causes of morbidity data quality issues, including the structure of data capture systems and the use of more consistent approaches to standards writing, such as those applied by the International Organisation for Standardisation (ISO), which is not investigated by this research.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106333"},"PeriodicalIF":4.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146167939","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":"The state of standardized musculoskeletal terminology for healthcare reuse:A scoping review","authors":"Melinda Wassell ,&nbsp;Kerryn Butler-Henderson ,&nbsp;Peter McCann ,&nbsp;Henry Pollard ,&nbsp;Salma Arabi ,&nbsp;Wei Wang ,&nbsp;Karin Verspoor","doi":"10.1016/j.ijmedinf.2026.106318","DOIUrl":"10.1016/j.ijmedinf.2026.106318","url":null,"abstract":"<div><h3>Objective</h3><div>Standardizing terminology offers opportunities for improved communication and care outcomes. With increasing adoption of clinical terminologies, questions remain about whether they adequately capture the scope of musculoskeletal (MSK) primary care practice. This scoping review examines global development efforts on MSK-relevant standardized terminology and its implementation in clinical practice.</div></div><div><h3>Methods</h3><div>A scoping review was conducted of 6 databases to May 2025. Identified studies (n = 3668) were included (n = 60) if they addressed standardized terminology relevant to the MSK primary care professions of chiropractic, osteopathy, and physiotherapy. Data were extracted on use cases, documentation of MSK information, alignment with national interoperability standards, and implementation status.</div></div><div><h3>Results</h3><div>Global development efforts span diverse MSK domains across condition types. Five studies achieved consensus around domain-specific terms (including tendinopathies, groin pain, and weight-bearing rehabilitation); in contrast, many studies developed extensive clinical terminology sets. Most studies (82.4%) address the development of terminologies, with few yet addressing how they have been implemented into clinical practice (2.7%).</div><div>Analysis revealed MSK clinicians require documentation beyond existing core interoperability data groups, including 1) function and movement, 2) pain characteristics, 3) psychosocial factors, 4) social determinants of health (environmental factors and participation barriers), 5) intervention effectiveness and clinical outcomes, and 6) person-centered factors.</div><div>Multiple barriers emerged, including technical (EHR integration, cognitive burden), workflow (time requirements, clinical value), professional (training, profession-specific terminology), and knowledge gaps (impact on care quality).</div></div><div><h3>Conclusion</h3><div>Extensive terminology development has begun yet gaps exist between development and clinical adoption. Terms evolve as research evolves; therefore, MSK professions should actively engage with interoperability groups to establish hierarchical ontologies that incorporate the identified data groups and balance standardization at higher conceptual levels with flexible lexicons to enable terminology growth over time. Establishing feedback mechanisms with EHR vendors to minimize clinicians’ cognitive burden will accelerate adoption and maximize clinical value.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106318"},"PeriodicalIF":4.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146159304","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 personalized and complex mHealth intervention for the universal prevention of Perinatal mental Disorders in routine maternal Care: Design and development of e-Perinatal app","authors":"Company-Córdoba Rosalba ,&nbsp;Caffieri Alessia ,&nbsp;Barquero-Jimenez Carlos ,&nbsp;Cruz-Cabrera Roberto ,&nbsp;De-Juan-Iglesias Paula ,&nbsp;Gil-Cosano José J. ,&nbsp;Goossens Lennert ,&nbsp;Nieto-Casado Francisco J. ,&nbsp;Ureña-Lorenzo Amalia ,&nbsp;Gómez-Gómez Irene ,&nbsp;Motrico Emma","doi":"10.1016/j.ijmedinf.2026.106290","DOIUrl":"10.1016/j.ijmedinf.2026.106290","url":null,"abstract":"<div><h3>Background</h3><div>Perinatal Mental Disorders (PMDs) are common during pregnancy and the first postpartum year, with negative consequences for women, their partners, and infants, as well as broader societal costs. While numerous interventions have been developed to prevent PMDs, there remains a need for a universal, personalized, and cost-effective solution integrated into routine maternal care. The <em>e-Perinatal</em> study aimed to address this gap. This paper describes the design of the <em>e-Perinatal</em> intervention, delivered via a dedicated mobile health app.</div></div><div><h3>Methods</h3><div>Guided by the Medical Research Council framework, the <em>e-Perinatal</em> app integrates Self-Determination Theory, Normalization Process Theory, and Patient and Public Involvement perspectives. Existing evidence was reviewed, and stakeholders participated in the co-development of digital micro-interventions (DMs). A clinical rule-based algorithm was implemented to generate personalized recommendations across four pathways (1) weekly content delivery, (2) user preferences, (3) individual risk profile, and (4) PMD monitoring.</div></div><div><h3>Results</h3><div>The <em>e-Perinatal</em> app includes: 1) DMs focused on psychological, physical activity, and healthy lifestyle domains; 2) a personalized recommendation engine; 3) a social support section; 4) mental health monitoring; 5) an ‘SOS’ button for assistance; and 6) an appointment reminder tool. In total, 332 evidence-based DMs were developed for women and their partners and delivered in text, audio, and video formats. A clinical rule-based algorithm tailors recommendations according to user characteristics and perinatal stage, employing adaptive content filtering to optimize personalization.</div></div><div><h3>Conclusion</h3><div>the <em>e-Perinatal</em> app is a personalized mHealth intervention to<!--> <!-->prevent PMDs within routine maternal care. The intervention combines evidence-based strategies, personalized recommendations, and adaptive digital content to prevent PMDs. Future research will assess effectiveness, implementation, and real-world impact of <em>e-Perinatal</em> intervention for PMD prevention.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106290"},"PeriodicalIF":4.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146133643","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":"2026-05-01","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}