International Journal of Medical Informatics最新文献

{"title":"Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review","authors":"Ruba Sulaiman ,&nbsp;Md.Ahasan Atick Faisal ,&nbsp;Maram Hasan ,&nbsp;Muhammad E.H. Chowdhury ,&nbsp;Faycal Bensaali ,&nbsp;Abdulrahman Alnabti ,&nbsp;Huseyin C. Yalcin","doi":"10.1016/j.ijmedinf.2025.105840","DOIUrl":"10.1016/j.ijmedinf.2025.105840","url":null,"abstract":"<div><h3>Background</h3><div>Transcatheter aortic valve implantation (TAVI) therapy has demonstrated its clear benefits such as low invasiveness, to treat aortic stenosis. Despite associated benefits, still post-procedural complications might occur. The severity of these complications depends on pre-existing clinical conditions and patient specific complex anatomical features. Accurate prediction of TAVI outcomes will assist in the precise risk assessment for patients undergoing TAVI. Throughout the past decade, different machine learning (ML) approaches have been utilized to predict outcomes of TAVI. This systematic review aims to assess the application of ML in TAVI for the purpose of outcome prediction<strong>.</strong></div></div><div><h3>Methods</h3><div>Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline was adapted for searching the PubMed and Scopus databases on ML use in TAVI outcomes prediction. Once the studies that meet the inclusion criteria were identified, data from these studies were retrieved and were further examined. 17 parameters relevant to TAVI outcomes were carefully identified for assessing the quality of the included studies.</div></div><div><h3>Results</h3><div>Following the search of the mentioned databases, 78 studies were initially retrieved, and 17 of these studies were included for further assessment. Most of the included studies focused on mortality prediction, utilizing datasets of varying sizes and diverse ML algorithms. The most employed ML algorithms were random forest, logistics regression, and gradient boosting. Among the studied parameters, serum creatinine, age, BMI, hemoglobin, and aortic valve mean gradient were identified as key predictors for TAVI outcomes. These predictors were found to be well aligned with established associations in current literature.</div></div><div><h3>Conclusion</h3><div>ML presents a promising opportunity for improving the success and safety of TAVI and enhancing patient-centered care. While currently retrospective studies with low generalizability and heterogeneity form the basis of ML TAVI research, future prospective investigations with highly heterogeneous patient TAVI cohorts will be critically important for firmly establishing the applicability of ML in predicting TAVI outcomes.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"197 ","pages":"Article 105840"},"PeriodicalIF":3.7,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430293","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":"AI-driven triage in emergency departments: A review of benefits, challenges, and future directions","authors":"Adebayo Da’Costa ,&nbsp;Jennifer Teke ,&nbsp;Joseph E. Origbo ,&nbsp;Ayokunle Osonuga ,&nbsp;Eghosasere Egbon ,&nbsp;David B. Olawade","doi":"10.1016/j.ijmedinf.2025.105838","DOIUrl":"10.1016/j.ijmedinf.2025.105838","url":null,"abstract":"<div><h3>Background</h3><div>Emergency Departments (EDs) are critical in providing immediate care, often under pressure from overcrowding, resource constraints, and variability in patient prioritization. Traditional triage systems, while structured, rely on subjective assessments, which can lack consistency during peak hours or mass casualty events. AI-driven triage systems present a promising solution, automating patient prioritization by analyzing real-time data, such as vital signs, medical history, and presenting symptoms. This narrative review examines the key components, benefits, limitations, and future directions of AI-driven triage systems in EDs.</div></div><div><h3>Method</h3><div>This narrative review analyzed peer-reviewed articles published between 2015 and 2024, identified through searches in PubMed, Scopus, IEEE Xplore, and Google Scholar. Findings were synthesized to provide a comprehensive overview of their potential and limitations.</div></div><div><h3>Results</h3><div>The review identifies substantial benefits of AI-driven triage, including improved patient prioritization, reduced wait times, and optimized resource allocation. However, challenges such as data quality issues, algorithmic bias, clinician trust, and ethical concerns are significant barriers to widespread adoption. Future directions emphasize the need for algorithm refinement, integration with wearable technology, clinician education, and ethical framework development to address these challenges and ensure equitable implementation.</div></div><div><h3>Conclusion</h3><div>AI-driven triage systems have the potential to transform ED operations by enhancing efficiency, improving patient outcomes, and supporting healthcare professionals in high-pressure environments. As healthcare demands continue to grow, these systems represent a vital innovation for advancing emergency care and addressing longstanding challenges in triage.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"197 ","pages":"Article 105838"},"PeriodicalIF":3.7,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143418801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting cancer survival at different stages: Insights from fair and explainable machine learning approaches","authors":"Tejasvi Sanjay Kamble ,&nbsp;Hongtao Wang ,&nbsp;Nicole Myers ,&nbsp;Nickolas Littlefield ,&nbsp;Leah Reid ,&nbsp;Cynthia S. McCarthy ,&nbsp;Young Ji Lee ,&nbsp;Hongfang Liu ,&nbsp;Liron Pantanowitz ,&nbsp;Soheyla Amirian ,&nbsp;Hooman H. Rashidi ,&nbsp;Ahmad P. Tafti","doi":"10.1016/j.ijmedinf.2025.105822","DOIUrl":"10.1016/j.ijmedinf.2025.105822","url":null,"abstract":"<div><h3>Objectives</h3><div>While prior machine learning (ML) models for cancer survivability prediction often treated all cancer stages uniformly, cancer survivability prediction should involve understanding how different stages impact the outcomes. Additionally, the success of ML-powered cancer survival prediction models depends a lot on being fair and easy to understand, especially for different stages of cancer. This study addresses cancer survivability prediction using fair and explainable ML methods.</div></div><div><h3>Methods</h3><div>Focusing on bladder, breast, and prostate cancers using SEER Program data, we developed and validated fair and explainable ML strategies to train separate models for each stage. These computational strategies also advance the fairness and explainability of the ML models.</div></div><div><h3>Results</h3><div>The current work highlights the important role of ML fairness and explainability in stage-specific cancer survivability prediction, capturing and interpreting the associated factors influencing cancer survivability.</div></div><div><h3>Conclusions</h3><div>This contribution advocates for integrating fairness and explainability in these ML models to ensure equitable, fair, interpretable, and transparent predictions, ultimately enhancing patient care and shared decision-making in cancer treatment.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"197 ","pages":"Article 105822"},"PeriodicalIF":3.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430276","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":"Utilization, challenges, and training needs of digital health technologies: Perspectives from healthcare professionals","authors":"Ruby Khan ,&nbsp;Sumbal Khan ,&nbsp;Hailah M. Almohaimeed ,&nbsp;Amany I. Almars ,&nbsp;Bakht Pari","doi":"10.1016/j.ijmedinf.2025.105833","DOIUrl":"10.1016/j.ijmedinf.2025.105833","url":null,"abstract":"<div><div><strong>Introduction:</strong> Digital health technology (DHTs), such as electronic health records (EHRs), mobile health apps, and remote monitoring systems, is revolutionizing contemporary healthcare by improving diagnosis, patient care, and operational efficiency. Notwithstanding these developments, infrastructure, technical assistance, and personnel training remain obstacles to the successful deployment of DHTs.</div><div><strong>Methods:</strong> 500 medical experts participated in a survey to evaluate the use, advantages, and challenges of DHTs. The frequency of DHT use, the perceived advantages, and the challenges—such as technical difficulties and a lack of training—were the main topics of the data gathered.</div><div><strong>Results:</strong> The most popular technology was mobile health apps (44.4%), followed by EHR systems and diagnostic tools (33.3%). Benefits reported included decreased administrative burden (50%) and increased diagnostic accuracy (46.2%). However, there significant obstacles were found, though: 63% of respondents said they had only received limited training, and 51.9% mentioned software bugs and network problems. Despite these obstacles, 63% of those surveyed reported increases in the effectiveness of healthcare delivery.</div><div><strong>Discussion:</strong> Our study finds a gap between the infrastructure needed for DHTs to be implemented successfully and their quick adoption. This study challenges the notion that adopting technology alone increases productivity by highlighting the importance of thorough technical assistance and staff training. These issues need to be resolved if DHTs are to be fully utilized for improved healthcare delivery and operational effectiveness.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"197 ","pages":"Article 105833"},"PeriodicalIF":3.7,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403335","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 fading structural prominence of explanations in clinical studies","authors":"Daniele Roberto Giacobbe,&nbsp;Matteo Bassetti","doi":"10.1016/j.ijmedinf.2025.105835","DOIUrl":"10.1016/j.ijmedinf.2025.105835","url":null,"abstract":"","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"197 ","pages":"Article 105835"},"PeriodicalIF":3.7,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402525","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":"Exploring the importance of clinical and sociodemographic factors on self-rated health in midlife: A cross-sectional study using machine learning","authors":"Hisrael Passarelli-Araujo","doi":"10.1016/j.ijmedinf.2025.105834","DOIUrl":"10.1016/j.ijmedinf.2025.105834","url":null,"abstract":"<div><h3>Background</h3><div>Self-rated health (SRH) is influenced by various factors, including clinical and sociodemographic characteristics. However, in the context of Brazil, we still lack a clear understanding of the relative importance of these factors and how they differ between men and women in midlife. Given the significance of gender equity in health, it is crucial to explore these differences to meet the specific needs of each group.</div></div><div><h3>Objective</h3><div>This study examines the importance of clinical and sociodemographic factors of SRH among middle-aged Brazilian adults and analyzes how they vary between men and women.</div></div><div><h3>Methods</h3><div>A cross-sectional analysis was conducted using data from the 2019 National Health Survey (PNS) with a representative sample of 31,926 middle-aged adults (40–59 years) living in private households on Brazilian territory. Five machine learning models—Naive Bayes, SVM, Logistic Regression, Random Forests, and XGBoost—were employed to analyze the data.</div></div><div><h3>Results</h3><div>The analysis revealed gender-specific patterns in SRH predictors. For men, education was the most critical factor, followed by diagnoses of physical and mental illnesses. For women, SRH was primarily influenced by chronic disease diagnoses, low education, and health insurance coverage. Alcohol consumption was a stronger predictor of poor SRH for men than women, likely due to cultural norms that promote higher alcohol use among men.</div></div><div><h3>Conclusion</h3><div>This study provides insights into midlife health disparities, emphasizing gender-specific factors influencing SRH. Machine learning demonstrated its value in uncovering nuanced patterns in health data, offering a powerful tool for public health research and policy.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"196 ","pages":"Article 105834"},"PeriodicalIF":3.7,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388314","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":"Serious Games for constipation management for people with intellectual disabilities: A scoping review and narrative synthesis","authors":"Serena Daniel ,&nbsp;Ruth Bishop ,&nbsp;Ellie Killner ,&nbsp;Alison Whight ,&nbsp;Sarah Lennard ,&nbsp;Stephen Howard ,&nbsp;Richard Laugharne ,&nbsp;Rohit Shankar","doi":"10.1016/j.ijmedinf.2025.105832","DOIUrl":"10.1016/j.ijmedinf.2025.105832","url":null,"abstract":"<div><h3>Introduction</h3><div>People with intellectual disability (PwID) are 2% of the UK population. Constipation and bowel movement (BM) problems (diarrhoea/faecal incontinence etc.) affects over a third of PwID and is a serious cause of morbidity and mortality. Pw ID rely heavily on outside support (family/professional carers/healthcare professionals), many of whom are ignorant to bowel related harms. There is significant stigma to discuss BM particularly constipation.</div><div>Serious Games (SG) are increasingly used for education of health needs. This review examines if game-based technology can assist improving knowledge and reducing stigma of BM problems particularly constipation.</div></div><div><h3>Objective</h3><div>To identify and gain evidence of SGs aimed at improving knowledge of BM management particularly constipation.</div></div><div><h3>Methods</h3><div>A systematic search of publications between 2010 and 2024 was conducted following the PRISMA ScR statement for scoping reviews. The search inclusion/exclusion criteria were designed and overseen by an information specialist. PUBMED, EMBASE and PsychINFO databases were searched. Extracted variables included SG title, co-production and expert involvement, target outcome, evaluation methodology, effectiveness, sustainability and game platform. Results were narratively synthesised.</div></div><div><h3>Results</h3><div>Of 2966 papers retrieved, three were selected for inclusion, none RCTs. All three included SGs aimed to teach BM management or recognition to healthcare workers/ professionals. Two studies evaluated game efficacy. No SGs were assessed after initial trials, none were implemented in clinical practice. Only one game successfully improved participant knowledge. All game creators consulted experts during game design, but none consulted patients. None discussed reducing stigma amongst their audience.</div></div><div><h3>Conclusion</h3><div>Only one of three SGs identified improved BM knowledge in healthcare workers/professionals and was not specific to PwID. There is potential to co-produce with PwID and their carers a SG to support BM problems particularly constipation to reduce stigma, improve outcomes and be a templar for other similarly vulnerable groups like those with dementia.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"196 ","pages":"Article 105832"},"PeriodicalIF":3.7,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal convolutional neural networks for the prediction of acute kidney injury in the intensive care","authors":"R. van Slobbe ,&nbsp;D. Herrmannova ,&nbsp;D.J. Boeke ,&nbsp;E.S. Lima-Walton ,&nbsp;A. Abu-Hanna ,&nbsp;I. Vagliano","doi":"10.1016/j.ijmedinf.2025.105815","DOIUrl":"10.1016/j.ijmedinf.2025.105815","url":null,"abstract":"<div><div>Increased monitoring of health-related data for ICU patients holds great potential for the early prediction of medical outcomes. Research on whether the use of clinical notes and concepts from knowledge bases can improve the performance of prediction models is limited. We investigated the effects of combining clinical variables, clinical notes, and clinical concepts. We focus on the early prediction of Acute Kidney Injury (AKI) in the intensive care unit (ICU). AKI is a sudden reduction in kidney function measured by increased serum creatinine (SCr) or decreased urine output. AKI may occur in up to 30% of ICU stays. We developed three models based on convolutional neural networks using data from the Medical Information Mart for Intensive Care (MIMIC) database. The models used clinical variables, free-text notes, and concepts from the Elsevier H-Graph. Our models achieved good predictive performance (AUROC 0.73-0.90). These models were assessed both when using Scr and urine output as predictors and when omitting them. When Scr and urine output were used as predictors, models that included clinical notes and concepts together with clinical variables performed on par with models that only used clinical variables. When excluding SCr and urine output, predictive performance improved by combining multiple modalities. The models that used only clinical variables were externally validated on the eICU dataset and transported fairly to the new population (AUROC 0.68-0.77). Our in-depth comparison of modalities and text representations may further guide researchers and practitioners in applying multimodal models for predicting AKI and inspire them to investigate multimodality and contextualized embeddings for other tasks. Our models can support clinicians to promptly recognize and treat deteriorating AKI patients and may improve patient outcomes in the ICU.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"196 ","pages":"Article 105815"},"PeriodicalIF":3.7,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143226609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluate the effect of virtual nurse-guided discharge education app on disease knowledge and symptom response in patients following coronary events","authors":"Ling Zhang ,&nbsp;Robyn Gallagher ,&nbsp;Huiyun Du ,&nbsp;Tracey Barry ,&nbsp;Jon Foote ,&nbsp;Tiffany Ellis ,&nbsp;Aarti Gulyani ,&nbsp;Robyn A. Clark","doi":"10.1016/j.ijmedinf.2025.105818","DOIUrl":"10.1016/j.ijmedinf.2025.105818","url":null,"abstract":"<div><h3>Background</h3><div>Pre-discharge patient education promotes better self-care and secondary prevention following acute coronary syndrome (ACS). Traditional methods do not adapt well to staff and patient time limitations and varied health literacy levels. Self-administered digital methods using engagement strategies may address these issues.</div></div><div><h3>Objectives</h3><div>To evaluate whether a co-designed, self-administered, virtual nurse avatar-guided patient education app can improve ACS knowledge, beliefs, and medication adherence, and be acceptable for patients and nurses.</div></div><div><h3>Methods</h3><div>A prospective pre-post-test study was used with patients recruited during hospitalisation for ACS and their associated nursing staff. Patients, alongside usual care, were provided with the education app on a tablet at discharge to use immediately and over the following month. Data were collected immediately following use and one-month post on heart disease knowledge, ACS symptom response attitudes and beliefs and medication adherence. User satisfaction data was collected from both patients and nurses.</div></div><div><h3>Results</h3><div>Participants included nurses (n = 22) and patients (n = 22) who were diagnosed with ST-elevation myocardial infarction (STEMI) (73 %), aged mean 59.7 years and 40 % had not completed high school.</div><div>Patients’ heart disease knowledge improved from pre to one-month post-use (15.7 vs 17.0; p &lt; 0.001) and from immediately post to one-month post-use (16.3 vs 17.0; p = 0.003). Patients’ ACS symptom knowledge and response beliefs improved from pre- to immediate post-use (13.8 vs 15.5; p = 0.008; 23.8 vs 25.1; p = 0.038), and to one-month post-use (13.8 vs 17.0; p &lt; 0.001; 23.8 vs 25.7; p = 0.025), and ACS symptom response attitudes improved from pre- to one-month post-use (15.8 vs 17.0; p = 0.036).</div><div>Patients and nurses rated the app’s presentation, content, usability, and usefulness highly; 86% of nurses thought the app would help with discharge education.</div></div><div><h3>Conclusion</h3><div>A co-designed, self-administered, virtual nurse avatar-guided education app can improve heart disease knowledge, attitudes, and beliefs following ACS with high nurse and patient acceptability.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"196 ","pages":"Article 105818"},"PeriodicalIF":3.7,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143226558","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 comparative analysis of trauma-related mortality in South Korea using classification models","authors":"Yookyung Boo ,&nbsp;Youngjin Choi","doi":"10.1016/j.ijmedinf.2025.105805","DOIUrl":"10.1016/j.ijmedinf.2025.105805","url":null,"abstract":"<div><h3>Background</h3><div>Reducing mortality among severe trauma patients requires the establishment of an effective emergency transportation system and the rapid transfer of patients to appropriate medical facilities. Machine learning offers significant potential to enhance the efficiency and quality of these emergency medical services.</div></div><div><h3>Methods</h3><div>A retrospective secondary analysis was conducted using region-specific trauma survey data. The analysis focused on socio-economic characteristics, mechanisms of injury, injury severity, and variables indicating the effectiveness of the emergency medical system in optimizing machine learning algorithms for predicting severe patient transportation decisions.</div></div><div><h3>Results</h3><div>Among the 8,769 patients with severe trauma, 7.2 % died in the hospital, with an average age of 50.06 years. The average injury severity score was 8.44, and the average time from accident reporting to arrival at the emergency medical facility was 55.39 min. The trend showed that as the level of the emergency medical institution increased, the patient transport time increased, while the mortality rate decreased. Additionally, XGBoost showed the best performance in mortality classification using a dataset sampled with SMOTE-ENN. Although the difference was minimal, undersampling slightly outperformed oversampling in the classification of emergency patients.</div></div><div><h3>Conclusion</h3><div>The treatment of emergency patients is influenced not only by transport time but also by the resources and staff levels of specialized emergency medical centers, which in turn affect survival rates. Furthermore, given the superior performance of composite sampling methods in analyzing imbalanced datasets, the importance of considering such imbalanced datasets in the analysis is evident.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"196 ","pages":"Article 105805"},"PeriodicalIF":3.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143226606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}