JMIR Medical Informatics最新文献

{"title":"Retraction: A Deep Neural Network for Estimating Low-Density Lipoprotein Cholesterol From Electronic Health Records: Real-Time Routine Clinical Application.","authors":"","doi":"10.2196/108367","DOIUrl":"10.2196/108367","url":null,"abstract":"","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e108367"},"PeriodicalIF":5.0,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13539173/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148801596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a Framework for Deidentified Japanese Electronic Health Record Narratives Using BERT: Balancing Privacy Protection and Reproducible Entity Extraction in Real-World Data.","authors":"Nobuo Mochizuki, Atsushi Ikeda, Hideyuki Ohshima, Yukika Fukuda, Kyoko Okuda, Ryota Komaki, Tomoyuki Matsunaga, Kenichi Kudo, Shintaro Goto, Motoki Amai, Yusuke Suzuki","doi":"10.2196/97411","DOIUrl":"https://doi.org/10.2196/97411","url":null,"abstract":"<p><strong>Background: </strong>Pharmacoepidemiologic studies using real-world data often lack clinical context, much of which is embedded in free-text electronic health records (EHRs). However, sharing EHR narratives is restricted by privacy requirements, and conventional deidentification approaches may reduce analytic utility and limit auditability.</p><p><strong>Objective: </strong>To develop and evaluate a framework that (1) extracts structured cancer-related entities from Japanese EHR free text using BERT (Bidirectional Encoder Representations from Transformers)-based natural language processing (NLP) and (2) generates deidentified analytic text that preserves contextual information to support post-hoc auditing under privacy constraints.</p><p><strong>Methods: </strong>We conducted a retrospective observational study using the DATuM IDEA database, which integrates unstructured EHR narratives with structured records (claims, prescriptions, and procedure/surgery records) from two hospitals in Japan from January 1, 2019, through June 30, 2025. Deterministic linkage was performed within ICI (Integrated Clinical Care Informatics, Inc.) prior to deidentification. The source text population comprised all linked progress notes from eligible oncology patients. Downstream recoverability analyses were conducted within a governance-constrained analyzable text cohort derived after deidentification and preprocessing. We quantified deidentification using masking rates and residual visible character density and evaluated downstream recoverability of TNM/stage-related mentions, internal logical consistency (M1 vs Stage IV), and proxy-based concordance using structured-data treatment proxies (ATC code L for systemic anticancer therapy and procedure-name keyword searches for cancer-directed interventions). A stratified manual audit of 400 deidentified narratives was conducted primarily to inspect for apparent residual direct identifiers, with retention of intended TNM and stage outputs reviewed as a secondary sanity check.</p><p><strong>Results: </strong>Among 51,876 patients with recorded diagnoses, 10,214 had neoplasms (ICD-10 C00-D48), and linked progress notes were available for 4,383 patients (82,863 records). The downstream analyzable text cohort (Layer C), used for downstream recoverability and proxy-based concordance analyses, comprised 3,689 patients and 38,841 documents. Of these, 1,339 patients (36.3%) and 11,914 documents (30.7%) had at least one recoverable TNM or stage mention (Layer D). Across all Layer C documents, marginal document-level recoverability was 24.2% for T, N, and M elements and 23.6% for stage. Conditional on Layer D, the corresponding recoverability rates were 0.788 for T, 0.787 for N, 0.788 for M, and 0.768 for stage. Median masking rates ranged from 9.9% to 15.0% across major cancer categories. M1 and Stage IV mention indicators showed an overall agreement of 94.7%, a positive percent agreement (PPA) of 54.8%, and a negative pe","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149018817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multicenter External Validation of an AI-Based Funduscopic Carotid Atherosclerosis Score and Assessment of Its Association With Coronary Artery Calcification: External Validation Study.","authors":"Changho Han, Jooyoung Chang, Jaewon Kim, Hyeokjong Lee, Kyae Hyung Kim, Seon Cho, Heesung Kwon, Suyoung Kim, Sang Min Park","doi":"10.2196/77335","DOIUrl":"10.2196/77335","url":null,"abstract":"<p><strong>Background: </strong>Screening for atherosclerosis is essential for early intervention, but conventional screening methods are often invasive and resource-intensive. As a result, there is growing interest in leveraging AI with noninvasive tools such as retinal fundus imaging to enable opportunistic cardiovascular risk assessment. The deep-learning funduscopic atherosclerosis score (DL-FAS) is an AI-derived biomarker, generated by a deep learning model, that was developed in a previous study to reflect the likelihood of carotid artery atherosclerosis from retinal fundus images.</p><p><strong>Objective: </strong>This study aimed to externally validate DL-FAS in a multicenter health checkup population and investigate its association with coronary artery calcification to gain deeper insights into its ability to reflect the broader systemic atherosclerotic burden.</p><p><strong>Methods: </strong>We used data from 108,982 participants in a Korean health checkup population who underwent retinal fundus imaging and at least one of either carotid artery sonography or coronary artery calcium scoring across 5 health-promotion centers operated by the Korea Association of Health Promotion between 2018 and 2021. Carotid atherosclerosis was defined by increased intima-media thickness (≥ 0.9 mm), atheroma, or stenosis. A coronary artery calcium score >0 indicated the presence of coronary artery calcification. DL-FAS (range 0-1) was generated for each fundus image, and the average score from both eyes was used as the final DL-FAS when available. The discriminative performance of DL-FAS for carotid atherosclerosis was assessed using the area under the receiver operating characteristic curve. We performed multivariable logistic regression, adjusted for the Pooled Cohort Equations (PCE) 10-year cardiovascular risk score, to quantify the associations between DL-FAS and both outcomes.</p><p><strong>Results: </strong>The area under the receiver operating characteristic curve for detecting carotid atherosclerosis was 0.700 (95% CI 0.697-0.703), confirming the generalizability of DL-FAS across multiple centers. In multivariable logistic regression adjusted for the PCE score, a 10% absolute increase in DL-FAS was associated with both carotid atherosclerosis (odds ratio 1.29, 95% CI 1.28-1.31) and coronary artery calcification (odds ratio 1.29, 95% CI 1.24-1.33). These associations remained significant among participants aged <60 years, as well as within the PCE-defined low- and moderate-risk subgroups, highlighting the potential utility of DL-FAS in populations that may benefit most from early detection and intervention.</p><p><strong>Conclusions: </strong>DL-FAS was externally validated in a large, multicenter health checkup dataset and was significantly associated with both carotid atherosclerosis and coronary artery calcification. These findings highlight its potential as a noninvasive biomarker associated with systemic atherosclerotic burden and cardiovascul","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e77335"},"PeriodicalIF":5.0,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13488915/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148801076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning-Based Risk Prediction of In-Hospital Mortality in Patients With Non-ST-Elevation Acute Coronary Syndrome at Various Stages of the Diagnostic Process: Observational Study.","authors":"Vyacheslav V Ryabov, Anastasiia K Nesova, Andrey A Vishnevskiy, Karina I Shakhgeldyan, Boris I Geltser","doi":"10.2196/89242","DOIUrl":"10.2196/89242","url":null,"abstract":"<p><strong>Background: </strong>Despite advances in understanding and treating non-ST-elevation acute coronary syndrome (NSTE-ACS), patients continue to experience high rates of adverse outcomes, particularly those with non-ST-segment elevation myocardial infarction, which remains a leading cause of cardiovascular mortality. Existing risk models may not fully reflect contemporary patient populations due to substantial changes in clinical profiles. Developing new machine learning (ML)-based risk calculators may improve the prediction of in-hospital mortality (IHM) at different stages of the diagnostic process, and ultimately improve patient outcomes.</p><p><strong>Objective: </strong>This study aimed to develop predictive models for IHM in patients with NSTE-ACS using ML methods and predictor sets obtained during the diagnostic process.</p><p><strong>Methods: </strong>This retrospective observational study included 1144 patients with NSTE-ACS admitted between 2019 and 2021. IHM occurred in 94 (8.1%) of 1144 patients. Predictive models were developed using multivariable logistic regression, Random Forest, XGBoost (Extreme Gradient Boosting), and CatBoost algorithms. Model performance was evaluated using the area under the receiver operating characteristic curve (ROC-AUC), area under the precision-recall curve, calibration metrics, and decision curve analysis.</p><p><strong>Results: </strong>A key feature of the developed models was their applicability at different stages of the diagnostic process, using predictors available at each specific stage. At admission, ML models achieved ROC-AUC values up to 0.93. After incorporating laboratory and echocardiographic data, predictive performance increased to ROC-AUC values of 0.94 to 0.95. The best-performing model demonstrated an ROC-AUC of 0.951 (95% CI 0.946-0.956), a sensitivity of 0.902 (95% CI 0.889-0.916), and a specificity of 0.891 (95% CI 0.886-0.896). The area under the precision-recall curve reached 0.685, and the Brier score was 0.0331, indicating good calibration. Random Forest models demonstrated greater clinical utility than the Global Registry of Acute Coronary Events score (<i>P</i><.001). Shapley Additive Explanations analysis identified the most significant predictors of mortality risk, including Killip class of acute heart failure, age, Charlson comorbidity index, creatinine level, and hematocrit level.</p><p><strong>Conclusions: </strong>ML-based models enabled accurate prediction of IHM in patients with NSTE-ACS at different stages of the diagnostic process and may improve risk stratification and clinical decision-making in real-world practice.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e89242"},"PeriodicalIF":5.0,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13489111/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Health Study Discoverability: Graph-Based Analysis Approach.","authors":"Lea Gütebier, Stefan Groß, Benjamin Winter, Max Blumenstock, Martin Dugas, Volkmar Liebscher, Dagmar Waltemath, Ron Henkel","doi":"10.2196/86812","DOIUrl":"10.2196/86812","url":null,"abstract":"<p><strong>Background: </strong>Efficiently finding and exploring relevant health studies is critical for informed, evidence-based health care. However, study information remains distributed across multiple resources, hindering interoperability, search, and reuse. Enhancing the findability of study data is a key challenge in promoting the findability, accessibility, interoperability, and reusability (FAIR) principles in health research.</p><p><strong>Objective: </strong>This study aimed to improve the findability and comparability of health studies by developing a semantically enriched graph-based framework that supports intuitive search and exploration for diverse stakeholders, including clinicians, researchers, and patients.</p><p><strong>Methods: </strong>We developed the BRAinS-Graph (\"Biomedical Knowledge Graph for Recommending and Analysing Health Studies\"), a semantically enriched knowledge base that integrates data from ClinicalTrials.gov, the Portal for Medical Data Models, the Unified Medical Language System, and MeSH into a single graph database. The framework applies an extract-transform-load process to integrate heterogeneous data structures and link related information across study resources and biomedical ontologies.</p><p><strong>Results: </strong>The BRAinS-Graph supports fine-grained, semantic searches across study metadata, eligibility criteria, and structural properties. Use cases illustrate its potential for clinicians, patients, and researchers, including analyses of study type distributions for meta-analyses and the identification of studies relevant to individual patients.</p><p><strong>Conclusions: </strong>By integrating heterogeneous study data into one interconnected knowledge base, the BRAinS-Graph improves the findability, accessibility, and reusability of study information. This work establishes a foundation for graph-based study recommendation systems and cross-institutional research infrastructures.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e86812"},"PeriodicalIF":5.0,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13484817/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Telemedical Supervision for Critical Anesthesia Scenarios: Randomized Controlled Simulation Study.","authors":"Jonas Roth, Verena Voigt, Michael Schauwinhold, Andreas Follmann, Michael Czaplik","doi":"10.2196/88056","DOIUrl":"10.2196/88056","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Telemedicine may improve access to specialized care, but its use for supervision during critical anesthesia situations remains underexplored. A standardized tele-supervision (TSV) solution for operating rooms (ORs) is lacking.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to evaluate a novel telemedical supervision system for critical anesthetic scenarios in a simulated OR environment and compare it to traditional on-site supervision. Specifically, adherence to standard operating procedures (SOPs), the number and modality of senior physician contacts, workload, and user perceptions were assessed.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;In this randomized controlled simulation study, 16 anesthesiology residents in their first 2 years of training at the Uniklinik Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen (Germany) were randomized using block randomization into 2 groups. The intervention group received remote support exclusively via a TSV system, while the control group used a conventional phone with on-site support. The telemedical system comprised an anesthesia workstation that integrated data from the patient monitor, anesthesia device, and syringe pumps using the Institute of Electrical and Electronics Engineers (IEEE) 11073 Service-Oriented Device Connectivity (SDC) standard and a mobile supervision workstation that enabled the senior physician to monitor multiple ORs and communicate via text, audio, and video. The simulated scenario involved a male patient aged 51 years undergoing an appendectomy who developed an anaphylactic reaction 3 minutes after receiving cefuroxime. Primary outcomes focused on the completion rate of necessary SOP measures. Secondary outcomes included workload, measured using the NASA Task Load Index (NASA-TLX), and participants' postscenario questionnaire responses. Statistical comparisons were performed using a Welch t test.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;All participants in both groups contacted the senior physician at least once. The control group had a mean of 6.44 (SD 4.80) SOP measures supported by the senior physician, whereas the intervention group had a mean of 5.00 (SD 3.06). The mean SOP completion rate was 92.5% (SD 0.04%) in the control group and 91.6% (SD 0.05%) in the intervention group, with no significant difference (t&lt;sub&gt;11.94&lt;/sub&gt;=0.439; P=.67). NASA-TLX scores revealed that compared to the control group, there was lower mental demand in the TSV group but higher temporal demand. Subjective evaluations indicated mixed preferences regarding on-site support; however, most participants acknowledged the TSV system as a viable alternative when on-site support was not feasible.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study demonstrated no statistically significant differences between the groups, indicating similar performance with high adherence to SOPs and comparable clinical decision-making in a simulated high-stakes environment. Despite increased temporal w","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e88056"},"PeriodicalIF":5.0,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13535306/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-Silo Federated Learning for Predicting Successful Mechanical Ventilation Weaning Across 5 Intensive Care Unit Databases: Retrospective Database Analysis.","authors":"Seyedmostafa Sheikhalishahi, Mathias Kaspar, Johanna Schwinn, Matthaeus Morhart, Philipp Simon, Ludwig Christian Hinske","doi":"10.2196/79713","DOIUrl":"10.2196/79713","url":null,"abstract":"<p><strong>Background: </strong>The prediction of weaning from mechanical ventilation (MV) can support clinical decision-making and help reduce the risk of weaning failure in intensive care units (ICUs). Cross-silo federated learning (FL) offers a promising approach to developing robust predictive models across multiple institutions without requiring the sharing of patient-level data.</p><p><strong>Objective: </strong>This study aimed to evaluate the feasibility and efficacy of FL for predicting successful weaning from MV across 5 diverse ICU databases and to compare its performance with local learning (LL) and centralized learning (CL) approaches that differ in their data-sharing requirements.</p><p><strong>Methods: </strong>We conducted a retrospective analysis using 5 ICU databases, namely the eICU Collaborative Research Database (eICU-CRD), Medical Information Mart for Intensive Care IV (MIMIC-IV), Universitätsklinikum Augsburg (UKA), High-Resolution ICU Dataset (HiRID), and Amsterdam University Medical Centers (AUMC), transforming clinical variables into the Observational Medical Outcomes Partnership (OMOP) Common Data Model. We defined successful weaning as a sustained reduction in positive end-expiratory pressure. We compared 3 learning approaches, FL, LL, and CL, using extreme gradient boosting (XGBoost). Performance was evaluated using the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), precision, recall, and F₁-score. All data use complied with local ethical regulations and institutional review board approvals, and all databases contained deidentified patient data accessed under institutional data use agreements and governed by applicable privacy regulations.</p><p><strong>Results: </strong>A total of 24,521 patients were included across 5 databases. The CL model achieved an AUROC of 0.81, AUPRC of 0.57, and F₁-score of 0.54 on pooled test data. The FL model achieved a macroaveraged AUROC of 0.74, AUPRC of 0.56, and F₁-score of 0.52. LL model performance varied across databases (AUROC=0.68-0.84, AUPRC=0.51-0.68, F₁-score=0.49-0.67), reflecting differences in data distribution and class balance.</p><p><strong>Conclusions: </strong>Our findings highlight performance differences between learning approaches for MV weaning prediction. LL models achieved the highest performance within their respective institutions (AUROC=0.68-0.84), CL achieved the highest performance on pooled test data (AUROC=0.81), and FL showed lower but reasonable performance while avoiding direct sharing of patient-level data (AUROC=0.74). The choice between approaches depends on institutional data-sharing constraints, local dataset characteristics, and acceptable performance thresholds. As privacy was not formally measured, these findings should be read as a performance comparison rather than a privacy-performance trade-off.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e79713"},"PeriodicalIF":5.0,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13525363/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148763803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Lactate-to-Albumin Ratio Trajectories and Outcomes in Sepsis-Associated Acute Kidney Injury: Evidence From MIMIC-IV and a Multicenter Chinese Cohort.","authors":"Fei Li, Xiaoxia Guo, Yunhao Li, Jin Zhang, Jingyi Wang, Pengchao Tian, Xi Zheng, Lifeng Huang, Yue Zheng, Wenxiong Li, Na Cui","doi":"10.2196/92838","DOIUrl":"10.2196/92838","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Sepsis-associated acute kidney injury (SA-AKI) is a frequent and life-threatening complication of sepsis. While the static lactate-to-albumin ratio (LAR) has prognostic value, its dynamic temporal evolution during early resuscitation and its utility for guiding clinical risk stratification remain underexplored.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The aim of the study is to identify distinct dynamic trajectories of LAR, evaluate their independent associations with adverse clinical outcomes, and construct a practical prognostic nomogram for patients with SA-AKI.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Data were extracted from the Medical Information Mart for Intensive Care IV database, including adult patients with SA-AKI and ≥ 3 lactate and albumin measurements within the first 72 hours of intensive care unit admission. A multicenter external validation cohort was assembled from 5 tertiary hospitals in Beijing, China. Group-based trajectory modeling identified distinct LAR trajectories. The primary outcome was 28-day mortality; secondary outcomes included 90-day mortality and continuous renal replacement therapy initiation. Trajectory-outcome associations were assessed using multivariable Cox and logistic regression models. Robustness was examined using restricted cubic splines, inverse probability of treatment weighting, weight truncation, doubly robust estimation, and subgroup analyses. Incremental predictive value beyond single baseline LAR was quantified, and a trajectory-integrated nomogram was developed and externally validated.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Among the 615 patients in the primary cohort, 3 LAR trajectories were identified: trajectory 1 (low-stable type), trajectory 2 (rapid-clearance type), and trajectory 3 (delayed-clearance type). In adjusted multivariable models, trajectory 3 (vs trajectory 1) was independently associated with increased 28-day mortality risk (hazard ratio 1.63, 95% CI 1.00-2.64; &lt;i&gt;P&lt;/i&gt;=.0496), 90-day mortality (hazard ratio 1.72, 95% CI 1.14-2.60; &lt;i&gt;P&lt;/i&gt;=.01), and continuous renal replacement therapy initiation (odds ratio 3.40, 95% CI 1.64-7.15; &lt;i&gt;P&lt;/i&gt;=.001). Conversely, the mortality risk for trajectory 2 did not differ significantly from trajectory 1. Sensitivity analyses supported these findings, although trajectory 3 associations attenuated in inverse probability of treatment weighting-weighted analyses. Incorporating trajectories improved predictive accuracy over single baseline LAR (continuous net reclassification improvement 0.170, integrated discrimination improvement 0.023; both &lt;i&gt;P&lt;/i&gt;=.01). In the external validation cohort (n=508), 3 analogous trajectories were identified. Trajectory 3 consistently conferred a higher mortality risk across both cohorts, whereas trajectory 2 was significantly associated with 28-day mortality only in validation. The nomogram exhibited modest discrimination in the external cohort (concordance index=0.612), acceptable calibration, and","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e92838"},"PeriodicalIF":5.0,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13478678/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148763219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-World Barriers to and Facilitators of Implementing AI-Based Clinical Decision Support Systems: Scoping Review.","authors":"Emma Bogner, Abby Thomas, Bishnu Bajgain, Cody van Rassel, Khara Sauro, Joon Lee","doi":"10.2196/89834","DOIUrl":"10.2196/89834","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Widespread and sustained uptake of AI-based clinical decision support systems (CDSSs) in real-world health care settings is uncommon, despite their potential to improve patient care and reduce clinician burnout. Although previous studies have examined determinants of implementing AI-based CDSSs, limited evidence has synthesized barriers and facilitators identified during actual clinical implementation and use.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The objectives of this scoping review were to (1) map and synthesize barriers to and facilitators of implementing AI-based CDSSs in real-world health care settings and (2) draw on this knowledge to inform future implementation strategies.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Five electronic databases (MEDLINE, Embase, CINAHL, APA PsycInfo, and the Cochrane Library) were searched from inception to May 2022. Eligible studies included primary research describing real-world implementation processes or reporting determinants (barriers and facilitators) of implemented AI-based CDSSs in any health care setting. Studies focused on non-decision support tasks, non-AI CDSSs, patient-facing tools, or development or effectiveness without implementation were excluded. No study design restrictions were applied. Full texts were reviewed to extract explicit statements describing determinants influencing implementation. These determinants were classified as barriers or facilitators and mapped to the Consolidated Framework for Implementation Research (CFIR) by 2 independent reviewers. A qualitative synthesis was conducted.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;After removing 4234 duplicate records, 10,875 articles were screened by title and abstract, which excluded 10,355 articles. After further exclusions based on full-text availability, 494 full-text articles were assessed for eligibility, of which 13 met the inclusion criteria. Nine of these studies reported explicit implementation determinants and were included in the CFIR-based synthesis. Studies were primarily conducted in the United States and involved multicenter implementation of machine learning-based CDSSs in critical care and emergency medicine settings. A total of 28 determinants (16 barriers and 12 facilitators) were identified. Barriers were most frequently mapped to the inner setting, innovation, and individuals domains, whereas facilitators were most frequently mapped to the implementation process and innovation domains. Common barriers included limited algorithm interpretability, data quality and management challenges, misalignment with clinical workflows, and insufficient user capability and motivation. Facilitators included early and ongoing assessment of end-user needs, stakeholder engagement, peer endorsement, and robust supporting evidence.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This review identified key determinants influencing the real-world implementation of AI-based CDSSs, highlighting the importance of system design, organization","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e89834"},"PeriodicalIF":5.0,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13472529/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148762539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Machine Learning-Based Model to Predict Overactive Bladder Risk Among US Women: Evidence From the National Health and Nutrition Examination Survey 2011-2018.","authors":"Guoqiang Huang, Shuangquan Lin","doi":"10.2196/80133","DOIUrl":"10.2196/80133","url":null,"abstract":"<p><strong>Background: </strong>Overactive bladder (OAB) is a prevalent condition, particularly among women, characterized by urinary urgency, often accompanied by frequency and nocturia. Traditional risk prediction methods for OAB are limited, as they fail to fully integrate multidimensional risk factors, including female reproductive history. Machine learning offers potential for enhanced predictive accuracy by using large-scale datasets like the National Health and Nutrition Examination Survey (NHANES).</p><p><strong>Objective: </strong>This study aimed to develop and validate a machine learning-based model to predict OAB risk in women, incorporating reproductive and sociodemographic factors, and to identify key predictors using interpretable methods.</p><p><strong>Methods: </strong>This retrospective observational study analyzed data from 7884 participants across 4 consecutive cycles (2011-2018) of the National Health and Nutrition Examination Survey. LASSO (least absolute shrinkage and selection operator) regression and univariate and multivariate logistic regression analyses were applied to identify key variables in the training set. Fourteen variables were selected via LASSO regression for model construction, among which age, BMI, ratio of family income to poverty threshold (PIR), age at menarche, and number of vaginal deliveries were identified as the most significant clinical predictors. The SHAP (Shapley Additive Explanations) method interpreted the optimal model, and restricted cubic spline (RCS) curves were used for dose-response analysis.</p><p><strong>Results: </strong>Five variables were identified as significant predictors. Among the 11 ML models, random forest (RF) demonstrated the highest predictive performance. The random forest model achieved an AUROC (area under the receiver operating characteristic curve) of 0.8536 (95% CI 0.8435-0.8638) in the training set and 0.6999 (95% CI 0.6768-0.7212) in the test set, indicating moderate predictive capability. SHAP analysis identified age, BMI, and the number of vaginal deliveries as the top 3 contributors to OAB risk. Both RCS and SHAP analyses revealed a positive association of age and BMI with OAB risk and a negative association with PIR. Additionally, RCS showed that the risk of OAB was higher with an earlier age at menarche and a greater number of vaginal deliveries.</p><p><strong>Conclusions: </strong>Integrating ML with SHAP interpretability provides a robust predictive tool for OAB, facilitating early identification and clinical management.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e80133"},"PeriodicalIF":5.0,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13468769/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148725404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}