[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118568-en":3,"doc-seo-118568-105":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},118568,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Sample Selection Bias in Machine Learning for Healthcare","Machine learning algorithms promise advances in personalised medicine, yet clinical adoption remains limited because biases can undermine the reliability of predictions. This article examines sample selection bias (SSB), where the study population poorly represents the target population, causing biased and potentially harmful decisions. Although SSB is known in broader literature, it has been studied only sparingly in healthcare ML. Existing methods often correct bias by balancing distributions, which can reduce predictive performance. The paper demonstrates these risks by evaluating SSB’s impact and proposes a new research direction based on the target population.","Latest updates: h􀀍ps://dl.acm.org/doi/10.1145/3761822  \nRESEARCH-ARTICLE  \nSample Selection Bias in Machine Learning for Healthcare  \nVINOD KUMAR CHAUHAN, University of Oxford, Oxford, Oxfordshire, U. K.  \nI am a Strathclyde Chancellor's Fellow in AI ( Lecturer/Assistant Professor) in the Department of Computer and Information Sciences at the University of Strathclyde. I also hold a Visiting Scholar position and the MPLS Enterprise and Innovation Fellowship (2025–26) at the University of Oxford, UK. My primary research interests are in Causality, Healthcare, and Artificial Intelligence, with a long-term vision of realising personalised treatments through data-driven Causal AI. Before joining Strathclyde, I was a Postdoctoral Researcher at the Department of Engineering Science, University of Oxford, and the Department of Engineering, University of Cambridge, where I worked for over six years. During this time, I had the opportunity to collaborate with world-renowned clinicians and industry leaders, including Boeing and RollsRoyce. At Cambridge,…(View more)  \nLEI CLIFTON, University of Oxford Medical Sciences Division, Oxford, Oxfordshire, U. K. ACHILLE SALAÜN, University of Oxford, Oxford, Oxfordshire, U. K.  \nHUIQI YVONNE LU, University of Oxford, Oxford, Oxfordshire, U. K.  \nKIM BRANSON, GlaxoSmithKline plc., Brentford, Middlesex, U. K.  \nPATRICK SCHWAB, GlaxoSmithKline plc., Brentford, Middlesex, U. K.  \nView all  \nOpen Access Support provided by:  \nUniversity of Oxford  \nNuffield Department of Medicine  \nOxford Suzhou Centre for Advanced Research  \nGlaxoSmith Kline plc.  \nUniversity of Oxford Medical Sciences Division  \nPDF Download  \n3761822.pdf  \n10 March 2026 Total Citations: 4  \nTotal Downloads: 1575  \nPublished: 13 October 2025  \nOnline AM: 18 August 2025  \nAccepted: 23 July 2025  \nRevised: 17 March 2025  \nReceived: 13 May 2024  \nCitation in BibTeX format  \nACM Transactions on Computing for Healthcare, Volume 6, Issue 4 (October 2025) h􀀤ps://doi.org/10 . 1145/3761822  \nEISSN: 2637-8051  \n.  \nSample Selection Bias in Machine Learning for Healthcare  \nVINOD KUMAR CHAUHAN, Department of Engineering Science, University of Oxford, Oxford, United Kingdom of Great Britain and Northern Ireland and Department of Computer and Information Sciences, University of Strathclyde, Glasgow, United Kingdom of Great Britain and Northern Ireland  \nLEI CLIFTON, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom of Great Britain and Northern Ireland  \nACHILLE SALAÜN and HUIQI YVONNE LU, Department of Engineering Science, University of Oxford, Oxford, United Kingdom of Great Britain and Northern Ireland  \nKIM BRANSON and PATRICK SCHWAB, GSK PLC, London, UK  \nGAURAV NIGAM, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom of Great Britain and Northern Ireland  \nDAVID A. CLIFTON, Department of Engineering Science, University of Oxford, Oxford, United Kingdom of Great Britain and Northern Ireland and Oxford-Suzhou Institute of Advanced Research (OSCAR), Suzhou, China  \nWhile machine learning algorithms hold promise for personalised medicine, their clinical adoption remains limited, partly due to biases that can compromise the reliability of predictions. In this article, we focus on sample selection bias (SSB), a specific type of bias where the study population is less representative of the target population, leading to biased and potentially harmful decisions. Despite being well-known in the literature, SSB remains scarcely studied in machine learning for healthcare. Moreover, the existing machine learning techniques try to correct the bias mostly by balancing distributions between the study and the target populations, which may result in a loss of predictive performance. To address these problems, our study illustrates the potential risks associated with SSB by examining SSB’s impact on the performance of machine learning algorithms. Most importantly, we propose a new res","cbCailhuUMop5zgQ","https://ap.wps.com/l/cbCailhuUMop5zgQ","pdf",14162834,1,25,"English","en",105,"# Introduction\n## Sample Selection Bias in Healthcare\n## Limitations of Existing Bias-Correction Methods\n## Proposed Research Direction Based on Target Population","[{\"question\":\"What is sample selection bias (SSB) in healthcare machine learning?\",\"answer\":\"SSB occurs when the study population is less representative of the target population. This mismatch leads to biased and potentially harmful decisions from ML predictions.\"},{\"question\":\"Why do current machine learning techniques for SSB have drawbacks?\",\"answer\":\"They often correct bias mainly by balancing distributions between the study and target populations. This may improve representativeness but can reduce predictive performance.\"},{\"question\":\"What does the article contribute to addressing SSB?\",\"answer\":\"It evaluates how SSB affects the performance of ML algorithms and proposes a new research direction for addressing SSB grounded in the target population.\"}]","Sample Selection Bias in Machine Learning for Healthcare | PDF",1785684279,63,{"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},"sample-selection-bias-in-machine-learning-for-healthcare","",{"@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/sample-selection-bias-in-machine-learning-for-healthcare/118568/",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 sample selection bias (SSB) in healthcare machine learning?","Question",{"text":75,"@type":76},"SSB occurs when the study population is less representative of the target population. This mismatch leads to biased and potentially harmful decisions from ML predictions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why do current machine learning techniques for SSB have drawbacks?",{"text":80,"@type":76},"They often correct bias mainly by balancing distributions between the study and target populations. This may improve representativeness but can reduce predictive performance.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the article contribute to addressing SSB?",{"text":84,"@type":76},"It evaluates how SSB affects the performance of ML algorithms and proposes a new research direction for addressing SSB grounded in the target population.","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"]