[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118610-en":3,"doc-seo-118610-105":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},118610,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Sample Selection Bias in Machine Learning for Healthcare - Research overview","Machine learning algorithms can improve personalized medicine, yet clinical adoption is constrained by biases that undermine prediction reliability. This work targets sample selection bias (SSB), where study participants poorly represent the intended target population, creating biased and potentially unsafe clinical decisions. Existing methods mainly correct SSB by balancing study and target distributions, which may reduce predictive performance. The study analyzes SSB’s effects on algorithm performance and introduces a target-population–focused research direction, including T-Net and MT-Net.","Sample 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 research direction for addressing SSB, based on the target population  \nThis work was supported in part by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC) and in part by the ITC InnoHK “Oxford-CityU Hong Kong Centre for Cerebrocardiovascular Health Engineering”(COCHE) . DAC was supported by an NIHR Research Professorship, an RAEng Research Chair, and the Pandemic Sciences Institute at the University of Oxford. GN is funded by the NIHR (Grant number 302607) for a doctoral research fellowship. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health, the InnoHK—ITC, or the University of Oxford.  \nAuthors’ Contact Information: Vinod Kumar Chauhan (corresponding author), 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; [e-mail: vinod.kumar@eng.ox.ac.uk](e-mail: vinod.kumar@eng.ox.ac.uk); Lei Clifton, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom of Great Britain and Northern Ireland; e-mail: [lei.clifton@ndph.ox.ac.uk](lei.clifton@ndph.ox.ac.uk); Achille Salaün, Department of Engineering Science, University of Oxford, Oxford, United Kingdom of Great Britain and Northern Ireland; e-mail: [achille.salaun@eng.ox.ac.uk](achille.salaun@eng.ox.ac.uk); Huiqi Yvonne Lu, Department of Engineering Science, University of Oxford, Oxford, United Kingdom of Great Britain and Northern Ireland; [e-mail: yvonne.lu@eng.ox.ac.uk](e-mail: yvonne.lu@eng.ox.ac.uk); Kim Branson, GSK PLC, London, UK; e-mail: [kim.m.branson@gsk.com](kim.m.branson@gsk.com); Patrick Schwab, GSK PLC, London, UK; [e-mail: patrick.x.schwab@gsk.com](e-mail: patrick.x.schwab@gsk.com); Gaurav Nigam, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom of Great Britain an","cbCainG4bc6ybc8i","https://ap.wps.com/l/cbCainG4bc6ybc8i","pdf",1406729,1,24,"English","en",105,"# Background and problem definition\n## Limits of existing bias-correction approaches\n# Proposed approach and methods\n## T-Net and MT-Net for SSB\n# Experiments and results\n## Performance drops and subpopulation differences\n## Robustness across dataset and selection settings\n# Contributions and implications","[{\"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, causing biased predictions. In healthcare settings this can translate into unreliable and potentially harmful decisions.\"},{\"question\":\"Why may distribution-balancing techniques be insufficient?\",\"answer\":\"Many existing methods correct bias by balancing distributions between study and target populations. This balancing can reduce predictive performance even if bias is mitigated.\"},{\"question\":\"What approach do the authors propose to address SSB?\",\"answer\":\"The authors propose T-Net and MT-Net. One network/task identifies a target subpopulation representative of the study population, while the second network/task makes predictions for that identified subpopulation.\"}]","Sample Selection Bias in Machine Learning for Healthcare - Research overview | PDF",1785684499,60,{"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-research-overview","",{"@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-research-overview/118610/",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, causing biased predictions. In healthcare settings this can translate into unreliable and potentially harmful decisions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why may distribution-balancing techniques be insufficient?",{"text":80,"@type":76},"Many existing methods correct bias by balancing distributions between study and target populations. This balancing can reduce predictive performance even if bias is mitigated.",{"name":82,"@type":73,"acceptedAnswer":83},"What approach do the authors propose to address SSB?",{"text":84,"@type":76},"The authors propose T-Net and MT-Net. One network/task identifies a target subpopulation representative of the study population, while the second network/task makes predictions for that identified subpopulation.","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},[93,97,101,105,109,114,119,122,127,130,134],{"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":29,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]