International Journal of Medical Informatics最新文献

{"title":"De-identification of clinical data: A systematic review of free text, image and tabular data approaches","authors":"Pedro Faustini ,&nbsp;Annabelle McIver ,&nbsp;Ryan Sullivan ,&nbsp;Mark Dras","doi":"10.1016/j.ijmedinf.2025.106225","DOIUrl":"10.1016/j.ijmedinf.2025.106225","url":null,"abstract":"<div><div><em>Background:</em> The digitisation of healthcare has generated vast amounts of data in various formats, including free-text notes, tabular records and medical images. This data is critical for research and innovation, but often contains sensitive information that must be de-identified to ensure patient privacy and regulatory compliance. Natural Language Processing (NLP) enables automated de-identification of sensitive information to safely share medical datasets.</div><div><em>Objective:</em> This study aims to systematically review the literature on NLP-based de-identification techniques applied to free-text medical reports, tabular data, and burned-in text within medical images over the past decade. It seeks to identify state-of-the-art methods, analyse how de-identification tasks are assessed, and find existing gaps for future research.</div><div><em>Methods:</em> We systematically searched five important databases (PubMed, Web of Science, DBLP, ACM and IEEE) for articles published from January 2015 to December 2024 (10 years) about de-identification of medical data in free text, tabular data and burned-in pixels in images. We filtered the articles based on their titles and abstracts against inclusion and exclusion criteria, followed by a quality filter.</div><div><em>Results:</em> From a set of 734 papers, 83 articles were deemed relevant. Most studies de-identify free text, with a few working with tabular data and a much scarcer number dealing with text embedded in the pixels of the images.</div><div><em>Conclusions:</em> De-identification techniques have evolved, with increased use of Language Models and a decline in recurrence-based neural networks. Off-the-shelf tools often require customisation for optimal performance. Most studies de-identify English content, supported by the prevalence of English datasets. Key challenges include the phenomenon of code-mixing (i.e., more than one language used in the same sentence) and the scarcity of available datasets for reproducibility.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"208 ","pages":"Article 106225"},"PeriodicalIF":4.1,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841724","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":"Physicians’ attitudes toward the patient summary in the Czech Republic: A national cross-sectional survey on awareness, use, and barriers","authors":"Petra Hospodková ,&nbsp;Jan Bruthans ,&nbsp;Adéla Englová","doi":"10.1016/j.ijmedinf.2025.106232","DOIUrl":"10.1016/j.ijmedinf.2025.106232","url":null,"abstract":"<div><h3>Introduction</h3><div>The Patient Summary (PS), a standardized subset of the electronic health record is designed to provide essential patient information for use in emergencies, unplanned care, and cross-border healthcare. While its technical development has progressed across Europe, little is known about real-world PS adoption and physician perceptions at the national level. This study explores the awareness, usage, and perceived barriers to the PS adoption among Czech physicians.</div></div><div><h3>Methods</h3><div>A cross-sectional online survey was distributed to all registered physicians in the Czech Republic between February and March 2025. The questionnaire assessed demographic characteristics, PS usage patterns, perceived benefits and barriers, and alignment with clinical practice. Descriptive statistics were calculated, and non-parametric tests (Wilcoxon rank-sum, Kruskal–Wallis) were used to examine differences by years of experience and medical specialty.</div></div><div><h3>Results</h3><div>A total of 1,739 responses were received (response rate: 4.14 %). Most respondents (66.4 %) reported not using the PS at all, and 72.1 % were unaware that their electronic medical record could be connected to the National Contact Point for eHealth. Only 1.7 % reported a current connection. There was no significant difference in PS use by years of clinical experience (P = 0.391), but a significant difference was observed across specialties (P &lt; 0.001), with the highest usage reported in intensive care medicine and internal medicine.</div></div><div><h3>Discussion and conclusion</h3><div>Despite recognized benefits, PS usage remains low in the Czech Republic, largely due to limited awareness and system integration. Targeted policy measures, improved communication, and enhanced digital training are needed to support effective adoption.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"208 ","pages":"Article 106232"},"PeriodicalIF":4.1,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145829159","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 role of digital twin technology in modern emergency care","authors":"David B. Olawade ,&nbsp;Osazuwa Ighodaro ,&nbsp;Emmanuel Oghenetejiri Erhieyovwe ,&nbsp;Nebere Elias Hankamo ,&nbsp;Ismail Tajudeen Hamza ,&nbsp;Claret Chinenyenwa Analikwu","doi":"10.1016/j.ijmedinf.2025.106229","DOIUrl":"10.1016/j.ijmedinf.2025.106229","url":null,"abstract":"<div><h3>Background</h3><div>Emergency care is operationally defined as time-critical acute care across pre-hospital services, emergency departments, and critical care units (excluding routine urgent care and elective admissions), demanding rapid decision-making under pressure. Digital twin technology, creating real-time virtual replicas through continuous data integration, represents a transformative shift in managing acute conditions, resource allocation, and outcome prediction in emergency medicine.</div></div><div><h3>Aim</h3><div>This review examines the current applications, benefits, challenges, and future directions of digital twin technology in emergency care and medicine, highlighting its potential to revolutionise emergency healthcare delivery.</div></div><div><h3>Method</h3><div>A comprehensive narrative literature review was conducted using PubMed, IEEE Xplore, Scopus, and Web of Science databases. Studies published between January 2015 and June 2025 focusing on digital twin applications in emergency departments, trauma care, critical care, and prehospital emergency services were included. Grey literature, conference proceedings, and technical reports were also reviewed to capture emerging developments.</div></div><div><h3>Results</h3><div>Digital twins demonstrate significant utility across multiple emergency care domains including patient monitoring, resource allocation, workflow optimisation, predictive analytics, and training simulations. Key applications include real time patient condition prediction, emergency department capacity management, trauma response coordination, and personalised treatment planning. Despite promising outcomes, implementation challenges persist, including data integration complexities, computational requirements, and regulatory considerations.</div></div><div><h3>Conclusion</h3><div>Digital twin technology holds substantial promise for enhancing emergency care delivery through improved decision support, resource optimisation, and predictive capabilities. Continued research, standardisation efforts, and interdisciplinary collaboration are essential for successful clinical integration and widespread adoption.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"208 ","pages":"Article 106229"},"PeriodicalIF":4.1,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799857","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 clinical-AI correlation for integrating artificial intelligence into stroke care: a systematized literature review and practice framework","authors":"João Brainer Clares de Andrade ,&nbsp;Thiago S. Carneiro ,&nbsp;George N. Nunes Mendes ,&nbsp;Joao Pedro Nardari dos Santos ,&nbsp;Jussie Correia Lima","doi":"10.1016/j.ijmedinf.2025.106233","DOIUrl":"10.1016/j.ijmedinf.2025.106233","url":null,"abstract":"<div><h3>Background and purpose</h3><div>The rapid integration of artificial intelligence (AI) into stroke care has outpaced many clinicians’ ability to critically evaluate and safely implement these tools. We conducted a systematized literature review and developed a practical framework to guide neurologists in the responsible integration of AI into stroke practice.</div></div><div><h3>Methods</h3><div>We performed a systematized review of PubMed, EMBASE, and gray literature (January 2018-June 2025) following adapted PRISMA guidelines. Search strategies combined AI-related terms with stroke care concepts. We assessed risk of bias using QUADAS-2, RoB 2, and ROBINS-I tools. Expert consultation with stroke neurologists and AI developers informed framework development.</div></div><div><h3>Results</h3><div>From 8,635 identified records, 152 studies met inclusion criteria (47 in quantitative synthesis). AI applications spanned large vessel occlusion detection (30 %), ASPECTS scoring (21 %), outcome prediction (18 %), hemorrhage detection (15 %), and treatment selection (16 %). Only 23% of studies showed low risk of bias, with main concerns including selection bias (29 %), confounding (38 %), and limited external validation (8 % prospective validation). The Clinical-AI Correlation Framework emphasizes three pillars: (1) problem identification and tool selection, (2) clinical correlation using Bayesian reasoning and topographic pattern recognition, and (3) continuous feedback and quality improvement.</div></div><div><h3>Conclusions</h3><div>Safe AI integration in stroke care requires structured clinical correlation, robust governance frameworks, and continuous monitoring. Our framework provides practical guidance for maintaining clinical judgment while leveraging AI capabilities, emphasizing human oversight for high-risk decisions and systematic documentation of AI-clinician interactions.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"208 ","pages":"Article 106233"},"PeriodicalIF":4.1,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145821995","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":"Application of Machine learning in predicting cancer complications using longitudinal Data: A systematic review and Meta-Analysis","authors":"Abu Sarwar Zamani ,&nbsp;Abdelwahed Motwakel Eltayeb ,&nbsp;Adel Alluhayb ,&nbsp;Md.Mobin Akhtar ,&nbsp;Rashid Ayub ,&nbsp;Mohammed Abdelmonem Ahmed Abdelrahim ,&nbsp;Sara Saadeldeen Ibrahim Mohamed ,&nbsp;Naved Ahmad","doi":"10.1016/j.ijmedinf.2025.106217","DOIUrl":"10.1016/j.ijmedinf.2025.106217","url":null,"abstract":"<div><div>Cancer prognosis of complications like metastasis, recurrence, and side effects of treatments is important to enhance patient prognosis. There is great potential in the use of ML on lifetime data for improving prediction accuracy in oncology; however, there is no systematic review of the subject. This SRMA is intended to assess the accuracy of ML models based on longitudinal studies for the estimation of cancer-related complications. The articles were identified from PubMed, Google Scholar, and IEEE Xplore databases for the years 2020 to 2024. Seven of the studies reviewed in the paper analyzed ML models that employed longitudinal data for cancer complication prognosis. The risk of bias of included studies was assessed using the Cochrane Risk of Bias tool, and for diagnostic accuracy, the QUADES 2 tool was used. Information on ML techniques, prediction accuracy, and results was obtained. The pooled area under the curve (AUC) for immune-related adverse events prediction was 0.78 (95% CI: 0.73–0.83). For cancer recurrence and mortality prediction, pooled AUCs ranged from 0.70 to 0.75. Machine learning models integrating clinical, genomic, and imaging data demonstrated superior predictive accuracy across various cancer types. Models predicting quality of life deterioration during treatment showed an AUC of 0.82. ML models applying longitudinal data effectively predict cancer complications with improved accuracy when integrating multimodal data. These models offer promising tools for clinical decision-making in oncology.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"208 ","pages":"Article 106217"},"PeriodicalIF":4.1,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799859","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":"Graph attention network with comorbidity connectivity embedding for post-traumatic epilepsy risk prediction using sparse time-series electronic health records","authors":"Priyadharsini Ramamurthy ,&nbsp;Zheng Han ,&nbsp;Dursun Delen ,&nbsp;Zhuqi Miao ,&nbsp;Andrew Gin ,&nbsp;Xiao Luo ,&nbsp;William Paiva","doi":"10.1016/j.ijmedinf.2025.106239","DOIUrl":"10.1016/j.ijmedinf.2025.106239","url":null,"abstract":"<div><h3>Background</h3><div>Traumatic brain injury (TBI) is a major risk factor for neurological disorders, including post-traumatic epilepsy (PTE), a debilitating condition associated with significant long-term consequences. The prognosis of PTE occurrence remains challenging due to the complex pathophysiology of PTE and the impracticality of traditional blood biomarker- or imaging-based screening for large populations. This study proposes a graph-based deep learning approach that leverages electronic health records (EHR) to enhance the predictive assessment of PTE risk.</div></div><div><h3>Methods</h3><div>We utilized Oracle Real-World Data (ORWD) to construct a Heterogeneous Graph Attention Network (HeteroGAT) that contains patient and diagnosis nodes, with temporal information represented using patients-to-diagnosis edges, and comorbidity connectivity embedded using diagnosis-to-diagnosis edges. The HeteroGAT was trained on a cohort of 1,598,998 TBI-only patients and 102,687 individuals who developed epilepsy after TBI. Model performance was evaluated using sensitivity, specificity, macro F1-score, and area under the receiver operating characteristic curve (AUC-ROC), benchmarked against traditional machine learning models. Attention scores of nodes were used to evaluate node importance. The capabilities of the HeteroGATs trained to differentiate early vs late PTE patients following TBI were also assessed.</div></div><div><h3>Results</h3><div>HeteroGAT significantly outperformed conventional models in PTE prediction by effectively integrating demographic data and comorbidity profiles spanning from 20 to 500 distinct conditions. The model’s multi-head attention mechanisms, in combination with learned comorbidity connectivity, enhanced its ability to capture complex dependencies within EHR data. HeteroGAT achieved an AUC-ROC of 0.80, outperforming the best-performing traditional model, random forest (AUC-ROC = 0.77). HeteroGAT also demonstrated capabilities in differentiating early and late PTEs. Ranking of nodes based on attention scores also identified predictors of PTE that are clinically relevant.</div></div><div><h3>Conclusion</h3><div>By modeling sparse EHR data through patient encounter embeddings, HeteroGAT effectively captures temporal and relational patterns in comorbidities critical for PTE prediction. Our findings highlight the potential of graph-based deep learning models, synergized with large-scale EHR data, in advancing personalized risk assessment, ultimately addressing the urgent need for more precise and proactive management of PTE in TBI patients.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"208 ","pages":"Article 106239"},"PeriodicalIF":4.1,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885680","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":"Ensemble machine learning for early mortality risk stratification in septic orthopedic trauma: an international cohort study","authors":"Jun Guo ,&nbsp;Fan Xiong ,&nbsp;Baisheng Sun ,&nbsp;Mingxing Lei ,&nbsp;Yong Qin","doi":"10.1016/j.ijmedinf.2025.106240","DOIUrl":"10.1016/j.ijmedinf.2025.106240","url":null,"abstract":"<div><h3>Background</h3><div>Sepsis represents a life-threatening complication in severe orthopedic trauma, significantly increasing short-term mortality risk. Despite the clinical urgency for early prognosis assessment, current predictive tools remain inadequate. To address this gap, this study used a machine learning (ML)-based framework for mortality risk stratification in this high-risk population.</div></div><div><h3>Methods</h3><div>This retrospective cohort study established ML models to predict 30-day all-cause mortality in critically ill patients with orthopedic trauma and sepsis. Data from 2,060 eligible patients were extracted from the intensive care unit (ICU) of Beth Israel Deaconess Medical Center (2008–2019) in the United State and randomly split into training (80 %) and internal validation (20 %) sets. After handling missing data and addressing class imbalance, seven ML algorithms (including CatBoost [Categorical Boosting], RF [Random Forest], and SVM [Support Vector Machine]) were trained and optimized using 10-fold cross-validation. Model performance was assessed based on discrimination (AUC [Area Under the Curve], accuracy, F1-score), calibration (Brier score, calibration slope), and clinical utility. The top-performing models were further validated on an independent external Chinese cohort (n = 273, 2020–2024).</div></div><div><h3>Results</h3><div>The study cohort had a mean age of 62.8 years and a 30-day mortality rate of 19.9 % (410/2060). Non-survivors were significantly older, had a higher comorbidity burden, and more severe physiological derangements. The LASSO analysis identified 16 prognostic variables, with age, hematologic parameters (RDW, WBC), SOFA scores, hemodynamic measures (SBP), and antihypertensive therapy emerging as significant predictors. Among all models, the CatBoost algorithm demonstrated superior performance in the internal validation set, achieving the highest AUC (0.955), accuracy (0.884), and F1-score (0.878), along with excellent calibration (Brier score: 0.081). A soft voting ensemble model, integrating the top three algorithms (CatBoost, RF, SVM), was subsequently constructed. In external validation, this ensemble model generalized robustly, maintaining strong discrimination (AUC: 0.842, Accuracy: 0.737) and calibration (Brier score: 0.173), outperforming the standalone CatBoost model. SHapley Additive exPlanations analysis provided interpretable, individualized risk assessments.</div></div><div><h3>Conclusions</h3><div>This study trains, optimizes, and evaluates a high-performing ML-based prediction model for 30-day mortality in patients with critical orthopedic trauma and sepsis. The CatBoost model and the soft voting ensemble, particularly the latter, demonstrates strong generalizability and clinical utility, offering a potential tool for early risk stratification and personalized management in this vulnerable population.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"208 ","pages":"Article 106240"},"PeriodicalIF":4.1,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885681","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 validation of a bleeding risk model for off-pump coronary artery bypass grafting: a multi-center retrospective cohort study","authors":"Zi Wang ,&nbsp;Runhua Ma ,&nbsp;Qiming Wang ,&nbsp;Fan Yang ,&nbsp;Xiaotong Xia ,&nbsp;Xiaoyu Li ,&nbsp;Qing Xu ,&nbsp;Yao yao ,&nbsp;Hongyi Wu ,&nbsp;Chunsheng Wang ,&nbsp;Qianzhou Lv","doi":"10.1016/j.ijmedinf.2025.106226","DOIUrl":"10.1016/j.ijmedinf.2025.106226","url":null,"abstract":"<div><h3>Background</h3><div>Perioperative bleeding is a major challenge in coronary artery bypass grafting (CABG). Existing bleeding risk models often lack specificity for off-pump CABG (OPCABG) patients.</div></div><div><h3>Objective</h3><div>This study aims to develop and validate a novel perioperative bleeding prediction model tailored for OPCABG patients.</div></div><div><h3>Methods</h3><div>This retrospective, multi-center cohort study was conducted using both internal and external validation cohorts. Fourteen different models, including Binary Logistic Regression, Random Forest, Decision Tree, Extra Trees, Adaptive Boosting, Extreme Gradient Boosting, Categorical Boosting, Gradient Boosting, Naive Bayes, Artificial Neural Network, Light Gradient Boosting Machine, K-nearest Neighbors, Support Vector Machine, and LogitBoost, were applied for model development. SHapley Additive exPlanations (SHAP) were used to interpret feature importance and the model’s outputs.</div></div><div><h3>Results</h3><div>The final model, CABG Bleeding Risk of 10 Variables (CABG-BR10), was built using Random Rorest. This model identified 10 key variables: antiplatelet drug discontinuation, N-terminal pro B-type natriuretic peptide, activated partial thromboplastin time, hemoglobin, urea, cardiac troponin T, estimated glomerular filtration rate, total bilirubin, fibrinogen, and international normalized ratio. In the internal and external validation cohorts, the model demonstrated solid performance with Receiver Operating Characteristic − Area Under the Curve values of 0.90 and 0.87, and Precision-Recall − Area Under the Curve values of 0.70 and 0.67, respectively. SHAP analysis identified key predictors of bleeding risk, and an online tool was developed to facilitate bleeding risk assessment.</div></div><div><h3>Conclusion</h3><div>The CABG-BR10 model accurately predicts perioperative bleeding risk in OPCABG patients, outperforming traditional scoring systems and providing interpretable, clinically relevant insights into bleeding risk factors.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"208 ","pages":"Article 106226"},"PeriodicalIF":4.1,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799858","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":"Construction and validation of a machine learning-based risk prediction model for invasive mechanical ventilation in AECOPD patients complicated with respiratory failure","authors":"Xin Jiang ,&nbsp;Ji Li ,&nbsp;Jingjing Ju ,&nbsp;Hao Ding ,&nbsp;Sufang Yang","doi":"10.1016/j.ijmedinf.2025.106244","DOIUrl":"10.1016/j.ijmedinf.2025.106244","url":null,"abstract":"<div><h3>Objective</h3><div>This study aimed to create and validate a machine learning (ML) model to predict the likelihood of invasive mechanical ventilation (IMV) in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) complicated by respiratory failure.</div></div><div><h3>Methods</h3><div>Data from patients diagnosed with AECOPD and respiratory failure were retrospectively extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV). A total of 551 cases were split 7:3 into a training set (385 cases) for model construction and an internal validation set (166 cases). The IMV served as the outcome event. Features were selected with the Boruta algorithm and least absolute shrinkage and selection operator (LASSO). Eight ML algorithms—XGBoost, decision tree (DT), random forest (RF), support-vector machine (SVM), LightGBM, CatBoost, Gaussian naïve Bayes (NB) and K-nearest neighbor (NN)—were trained with 10-fold cross-validation. Model performance was assessed by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, F1 score, calibration curve, decision curve and clinical impact curve. An external validation cohort of 100 AECOPD-respiratory failure patients admitted to Baoying People’s Hospital between January 2020 and August 2025 was collected. The final best model was interpreted with SHapley Additive exPlanations (SHAP) to clarify feature importance and decision logic, and an interactive dynamic nomogram was plotted to increase readability.</div></div><div><h3>Results</h3><div>Boruta plus LASSO identified total calcium, partial pressure of oxygen (PO₂), oxygen saturation (SpO₂) and sepsis as significant predictors. XGBoost outperformed the other algorithms, achieving an internal validation accuracy of 72.2<!--> <!-->%, sensitivity of 64.6<!--> <!-->%, specificity of 79.8 %, F1 score of 69.7<!--> <!-->% and AUC of 0.813 (95<!--> <!-->% CI 0.748–0.878). The external validation accuracy reached 76.4<!--> <!-->%, the sensitivity reached 82.6<!--> <!-->%, the specificity reached 70.0<!--> <!-->%, the F1 score reached 78.7<!--> <!-->%, and the AUC reached 0.840 (95<!--> <!-->% CI 0.801–0.879). SHAP analysis further indicated that PO₂ and SpO₂ were the primary drivers of model decisions. An interactive dynamic nomogram was successfully constructed.</div></div><div><h3>Conclusion</h3><div>IMV in AECOPD patients with respiratory failure was associated with total calcium, PO₂, and SpO₂ levels and sepsis. The developed XGBoost model demonstrated good predictive value for IMV in this clinical population.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"208 ","pages":"Article 106244"},"PeriodicalIF":4.1,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145866463","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-based diagnosis of autism spectrum disorder in children and adolescents using eye-tracking data: a systematic review and meta-analysis","authors":"Wenhao Han ,&nbsp;Xinyu Yang ,&nbsp;Xin Li ,&nbsp;Jiacheng Wang ,&nbsp;Juan Liu ,&nbsp;Wei Pang","doi":"10.1016/j.ijmedinf.2025.106235","DOIUrl":"10.1016/j.ijmedinf.2025.106235","url":null,"abstract":"<div><h3>Objective</h3><div>Eye-tracking technology has been increasingly investigated as an objective approach for distinguishing individuals with Autism Spectrum Disorder (ASD) from typically developing (TD) individuals. Artificial intelligence and machine learning (ML) methods have been widely applied to support ASD diagnosis and treatment, and prior studies suggest that ML models leveraging eye-tracking data can achieve high diagnostic accuracy. This systematic review and meta-analysis aimed to evaluate the diagnostic performance of machine-learning models using eye-tracking data to distinguish children and adolescents with ASD from TD peers.</div></div><div><h3>Methods</h3><div>We systematically searched PubMed, Embase, Web of Science, IEEE Xplore, Scopus, and the Cochrane Library from inception to August 3, 2025. We included studies that applied ML methods to eye-tracking data to distinguish children with ASD from TD children. We extracted data on participant characteristics, model performance, eye-tracking protocols, and machine-learning algorithms. The review protocol was registered in PROSPERO (CRD420251162462).</div></div><div><h3>Results</h3><div>We identified 1,045 records, of which 25 studies were included in the meta-analysis. The included studies comprised 2,319 participants, with sample sizes ranging from 32 to 529 per study. The pooled accuracy, sensitivity, and specificity of machine-learning models using eye-tracking data to distinguish children with ASD from TD children were 85 % (95 % CI, 81–89 %), 86 % (95 % CI, 82–89 %), and 86 % (95 % CI, 79–91 %), respectively. These results suggest that eye-tracking–based machine-learning approaches have good diagnostic performance for identifying ASD.</div></div><div><h3>Conclusion</h3><div>Eye-tracking–based machine-learning approaches show considerable potential for distinguishing children with ASD from TD children. However, the robustness and generalizability of these findings are limited by the lack of external validation, small sample sizes, and substantial between-study heterogeneity. To establish generalizability, future research should prioritize standardized eye-tracking paradigms and large-scale, prospective, multicenter study designs with external validation. Such efforts may facilitate the translation of these models into clinical practice as objective and efficient adjunctive screening tools.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"208 ","pages":"Article 106235"},"PeriodicalIF":4.1,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885672","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}