[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116860-en":3,"doc-seo-116860-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116860,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Ensemble machine learning methods in screening electronic health records - A scoping review","Electronic health records enable machine learning to pre-identify undiagnosed individuals likely to have specific diseases for screening and case finding, potentially reducing the number needed to screen while saving costs. Ensemble machine learning combines multiple prediction estimates and is often reported to outperform non-ensemble approaches, yet no review had systematically summarised ensemble types in medical pre-screening. This scoping review searched EMBASE and MEDLINE, compiling 145 eligible studies under PRISMA guidance to compare model use and reported performance.","Review Article  \nEnsemble machine learning methods in screening electronic health records: A scoping review  \nChristophe AT Stevens1 , Alexander RM Lyons1, Kanika I Dharmayat1, Alireza Mahani2, Kausik K Ray1, Antonio J Vallejo-Vaz1,3,4,* and Mansour TA Sharabiani5,*  \nAbstract  \nDIGITAL HEALTH Volume 9: 1–17  \n© The Author(s) 2023  \nArticle reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/20552076231173225](DOI: 10.1177/20552076231173225)[ ](DOI: 10.1177/20552076231173225)[journals.sagepub.com/home/dhj](journals.sagepub.com/home/dhj)  \nBackground: Electronic health records provide the opportunity to identify undiagnosed individuals likely to have a given disease using machine learning techniques, and who could then benefit from more medical screening and case finding, reducing the number needed to screen with convenience and healthcare cost savings. Ensemble machine learning models combining multiple prediction estimates into one are often said to provide better predictive performances than non-ensemble models. Yet, to our knowledge, no literature review summarises the use and performances of different types of ensemble machine learning models in the context of medical pre-screening.  \nMethod: We aimed to conduct a scoping review of the literature reporting the derivation of ensemble machine learning models for screening of electronic health records. We searched EMBASE and MEDLINE databases across all years applying a formal search strategy using terms related to medical screening, electronic health records and machine learning. Data were collected, analysed, and reported in accordance with the PRISMA scoping review guideline.  \nResults: A total of 3355 articles were retrieved, of which 145 articles met our inclusion criteria and were included in this study. Ensemble machine learning models were increasingly employed across several medical specialties and often outperformed non-ensemble approaches. Ensemble machine learning models with complex combination strategies and heterogeneous classifiers often outperformed other types of ensemble machine learning models but were also less used. Ensemble machine learning models methodologies, processing steps and data sources were often not clearly described.  \nConclusions: Our work highlights the importance of deriving and comparing the performances of different types of ensemble machine learning models when screening electronic health records and underscores the need for more comprehensive reporting of machine learning methodologies employed in clinical research.  \nKeywords  \nEnsemble machine learning, supervised machine learning, mass screening, electronic health records, scoping review  \nSubmission date: 29 July 2022; Acceptance date: 14 April 2023  \n\n| 1Imperial Centre for Cardiovascular Disease Prevention (ICCP), Department of Primary Care and Public Health, School of Public Health, Imperial College |  |\n| --- | --- |\n| London, London, UK\u003Cbr>2Quantitative Research, Davidson Kempner Capital Management, New York, NY, USA |  |\n| 3Department of Medicine, Faculty of Medicine, University of Seville, Sevilla, Spain |  |\n| 4Clinical Epidemiology and Vascular Risk, Instituto de Biomedicina de Sevilla (IBiS), IBiS/Hospital Universitario Virgen del Sevilla, Spain | Rocío/Universidad de Sevilla/CSIC, |\n| 5Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, UK\u003Cbr>*Antonio J Vallejo-Vaz and Mansour TA Sharabiani should be considered joint senior authors.\u003Cbr>Corresponding author:\u003Cbr>Christophe AT Stevens, Imperial Centre for Cardiovascular Disease Prevention, Department of Primary Care and Public Charing Cross Campus, The Reynolds Building, St Dunstan’s Road, London W6 8RP, UK.\u003Cbr>Email: [christophe.stevens@imperial.ac.uk](christophe.stevens@imperial.ac.uk) | Health, Imperial College London, |\n\nCreative Commons CC BY: This article is distributed under the terms","cbCaishG1IgBgzv7","https://ap.wps.com/l/cbCaishG1IgBgzv7","pdf",734332,1,17,"English","en",105,"# Abstract\n# Background\n# Method\n# Results\n# Conclusions\n# Introduction","[{\"question\":\"What is the purpose of using ensemble machine learning with electronic health records in medical screening?\",\"answer\":\"Ensemble machine learning supports pre-screening by identifying undiagnosed individuals likely to have a disease, guiding further screening or diagnostic confirmation. It aims to reduce the number needed to screen and related costs while improving predictive performance.\"},{\"question\":\"How was the scoping review conducted?\",\"answer\":\"The review searched EMBASE and MEDLINE across all years using a formal search strategy covering medical screening, electronic health records, and machine learning. Included data were collected and reported according to the PRISMA scoping review guideline.\"},{\"question\":\"What were the main findings about ensemble model performance and reporting quality?\",\"answer\":\"Across 145 included studies, ensemble methods were increasingly used and often outperformed non-ensemble approaches. Models using complex combination strategies and heterogeneous classifiers more frequently performed better, but ensemble methodology, processing steps, and data sources were often insufficiently described.\"}]","Ensemble machine learning methods in screening electronic health records - A scoping review | PDF",1785672114,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"ensemble-machine-learning-methods-in-screening-electronic-health-records-a-scoping-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/ensemble-machine-learning-methods-in-screening-electronic-health-records-a-scoping-review/116860/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the purpose of using ensemble machine learning with electronic health records in medical screening?","Question",{"text":75,"@type":76},"Ensemble machine learning supports pre-screening by identifying undiagnosed individuals likely to have a disease, guiding further screening or diagnostic confirmation. It aims to reduce the number needed to screen and related costs while improving predictive performance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the scoping review conducted?",{"text":80,"@type":76},"The review searched EMBASE and MEDLINE across all years using a formal search strategy covering medical screening, electronic health records, and machine learning. Included data were collected and reported according to the PRISMA scoping review guideline.",{"name":82,"@type":73,"acceptedAnswer":83},"What were the main findings about ensemble model performance and reporting quality?",{"text":84,"@type":76},"Across 145 included studies, ensemble methods were increasingly used and often outperformed non-ensemble approaches. Models using complex combination strategies and heterogeneous classifiers more frequently performed better, but ensemble methodology, processing steps, and data sources were often insufficiently described.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]