International Journal of Medical Informatics最新文献

{"title":"Digital literacy training within interventions to support older adults with cardiovascular disease in using technologies: a systematic review","authors":"Kathy L. Rush ,&nbsp;Cherisse L. Seaton ,&nbsp;Rowan Ross ,&nbsp;Taylor Robertson ,&nbsp;Angeliki-Iliana Louloudi ,&nbsp;Peter Loewen ,&nbsp;Kristen R. Haase ,&nbsp;Jennifer Jakobi ,&nbsp;Robert Janke","doi":"10.1016/j.ijmedinf.2026.106312","DOIUrl":"10.1016/j.ijmedinf.2026.106312","url":null,"abstract":"<div><h3>Background</h3><div>Advancement in digitalization in the health sector have created numerous opportunities for cardiovascular disease (CVD) self-management but also challenges, especially for older adults with lower digital health literacy. Reviews have examined impacts of digital health technology interventions on health outcomes without examining the role of training provided. The aim of this review is to synthesize evidence about the impacts of digital literacy training (DLT) and its characteristics as a component of digital interventions related to cardiovascular health on patient reported outcome and experience measures among older adults with CVD.</div></div><div><h3>Methods</h3><div>In accordance with the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a search of MEDLINE, EMBASE, CINAHL, and PsycINFO databases for articles published between inception to March 31, 2025 was conducted. Empirical studies reporting digital health technology training with adults (<em>M</em> age 60 + years) with CVD were eligible for inclusion. Articles included were quality-rated using the Mixed Methods Appraisal Tool. Data were extracted according to the DLT and health technologies alongside patient-reported outcome (i.e technology- and health-related) and experience measures.</div></div><div><h3>Results</h3><div>Of the 56 included studies (totaling 7698 participants), DLT varied considerably, with 51 describing in-person training. Two studies (totaling 519 participants) examined the role of training with positive impacts on technology- and health-related outcomes. In many of the remaining studies, positive technology-related outcomes were evident but could not be linked back to DLT separate from the overall intervention. In studies (n = 10) where training was evaluated, feedback from patients largely affirmed the training was needed.</div></div><div><h3>Discussion</h3><div>The collective evidence suggests DLT overall is useful and needed in digital interventions for older adults with CVD. More work is needed to elucidate the distinct role of DLT characteristics and to determine for whom and under what conditions DLT impacts health and technology-related outcomes.</div></div><div><h3>Registration</h3><div>The protocol for this review was registered Aug 12, 2024 in Open Science Framework (OSF) (See: https://osf.io/unhd9)</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106312"},"PeriodicalIF":4.1,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146133684","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":"2026-02-05","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":"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":"2026-02-05","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":"2026-02-05","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":"2026-02-04","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":"Clinical-radiological machine learning model for non-invasive diagnosis and stratification of peripheral artery disease: a multicenter study","authors":"Bowen Hou ,&nbsp;Jinhan Qiao ,&nbsp;Zheng Ran ,&nbsp;Yitong Li ,&nbsp;Zhongyichen Huang ,&nbsp;Xiaolong Luo ,&nbsp;Xiaoming Li","doi":"10.1016/j.ijmedinf.2026.106338","DOIUrl":"10.1016/j.ijmedinf.2026.106338","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Peripheral artery disease (PAD) is an atherosclerotic disorder prevalent in the elderly that leads to peripheral function decline and body composition changes. Current diagnostic approaches lack sensitivity for early PAD detection and staging. This study aimed to develop and validate machine learning (ML) models of clinical and CT-based radiological features to improve PAD diagnosis and severity stratification.</div></div><div><h3>Methods</h3><div>A retrospective multicenter study was conducted using data from two institutions. Clinical and radiological features (including volumetric body composition and muscle texture parameters extracted from calf and thigh segments) were analyzed. Participants were randomly divided into training (70%) and test (30%) sets, stratified by PAD status. Models with different ML algorithms were developed and compared. Model interpretability was assessed with Shapley additive explanation (SHAP) analysis, and performance was evaluated through receiver operating characteristic analysis, Hosmer-Lemeshow testing, Brier score and calibration curves.</div></div><div><h3>Results</h3><div>This study comprised 342 participants, divided into training (n = 176), test set (n = 76) from Institute 1, external validation (n = 90) from Institute 2. Three models were developed: clinical model (CM), radiological model (RM), and combined clinical-radiological model (CRM). The calf-based CRM using random forest algorithm achieved area under the curves of 0.871 (training), 0.870 (test), and 0.828 (validation), demonstrating good calibration (<em>p</em> ≥ 0.05) and the low Brier score. SHAP analysis visually interpreted feature contributions toward PAD diagnosis and staging.</div></div><div><h3>Conclusions</h3><div>The CRM model effectively integrated calf-derived radiological and clinical features into a noninvasive, interpretable tool for PAD diagnosis and severity stratification, demonstrating strong clinical applicability.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106338"},"PeriodicalIF":4.1,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146133629","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":"2026-02-03","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":"Harmonizing patient-reported outcome measures for nasal complaints using traditional and machine learning methods","authors":"Miljan Jović ,&nbsp;Esther Hof ,&nbsp;Maryam Amir Haeri ,&nbsp;Jasper J. Hoorweg ,&nbsp;Stéphanie M. van den Berg","doi":"10.1016/j.ijmedinf.2026.106319","DOIUrl":"10.1016/j.ijmedinf.2026.106319","url":null,"abstract":"<div><h3>Background</h3><div>Nasal obstruction measurement instruments are widely used in the field of nasal surgery. There are various scales that measure nasal obstruction and they differ regarding the number of items, their wording, and the type of response options. In order to pool the data and analyze it together, it is necessary to harmonize it so that we can compare participants’ nasal obstruction scores irrespective of instrument they filled in. Data harmonization is still not used in the field of nasal obstruction assessment.</div></div><div><h3>The Aim</h3><div>The aim of this study was to find the best harmonization method in terms of predicting the scores on a target instrument based on the scores from another instrument as precise as possible in the case of four different nasal complaints instruments. A method was sought to find a transformation of scores on the NOSE, Utrecht-Q and SCHNOS that makes them equivalent to ENFAS scores.</div></div><div><h3>Methods</h3><div>A total of 1324 unique patients completed all four measurement instruments. We tried linear equating, Item Response Theory (IRT), and the following machine learning methods: linear regression, random forest regression, support vector machine regression, and neural network. We used the root-mean-square error (RMSE) of differences between predicted and observed scores to evaluate the quality of harmonization in 5-fold cross-validation.</div></div><div><h3>Results</h3><div>The ML methods gave overall the best results (the lowest RMSEs) and outperformed IRT (which is considered as a common choice for data harmonization in psychometrics).</div></div><div><h3>Conclusion</h3><div>The ML methods led to the best quality of the results, confirming their strong potential for data harmonization. This study shows that next to linear equating and IRT that are commonly used for data harmonization, we can also use ML methods for the same purpose and, by doing so, to even increase the quality of the harmonization in certain use cases.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106319"},"PeriodicalIF":4.1,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146133655","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":"2026-02-02","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":"Using causal rule mining to identify opportunities for value improvement in regional CABG care: A proof-of-concept study","authors":"Sophie van Heuveln ,&nbsp;Gijs J. van Steenbergen ,&nbsp;Mileen R.D. van de Kar ,&nbsp;Erwin S.H. Tan ,&nbsp;Mohamed A. Soliman-Hamad Veghel ,&nbsp;Rik Eshuis ,&nbsp;Lukas R.C. Dekker ,&nbsp;Dennis van Veghel","doi":"10.1016/j.ijmedinf.2026.106317","DOIUrl":"10.1016/j.ijmedinf.2026.106317","url":null,"abstract":"<div><h3>Objective</h3><div>To explore the potential of causal rule mining (CRM) as a complementary method to outcome monitoring in identifying plausible causal patterns that may explain undesired clinical outcomes or elevated care consumption in cardiac surgery.</div></div><div><h3>Methods</h3><div>In this proof-of-concept study, CRM was applied to data from 1,068 patients who underwent elective isolated coronary artery bypass grafting between January 2016 and March 2021 at a single heart center and its referral network in the Netherlands. Outcomes of interest included: 1-year and 120-day mortality, in-hospital stroke, 30-day deep sternal wound infection (DSWI), 30-day re-explorations, 1-year coronary reinterventions, event-free survival, 30-day emergency department (ED) visits, postoperative length of stay, and preoperative fractional flow reserve (FFR) testing. Causal rules were considered relevant if both the odds ratio (OR) and its 95 % confidence interval (CI) were &gt; 1. Identified rules were independently reviewed by clinical experts.</div></div><div><h3>Results</h3><div>CRM identified 114 significant rules. Five rules were rated as ‘new and interesting’ and two additional rules were included based on special interest. In follow-up discussions, clinical experts agreed that three rules warrant further clinical investigation: (1) the absence of fractional flow reserve (FFR) testing reducing the likelihood of coronary reintervention, (2) absence of red blood cell (RBC) transfusion during admission reducing the likelihood of 30-day re-explorations, and (3) RBC transfusion increasing the likelihood of 30-day re-explorations.</div></div><div><h3>Conclusion</h3><div>CRM helped identify potential explanations for certain outcomes and care consumption, providing structured input for hypothesis-driven quality improvement and supporting efforts to enhance patient value.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106317"},"PeriodicalIF":4.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146121141","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}