[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118999-en":3,"doc-seo-118999-105":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},118999,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Sample Selection Bias in Machine Learning for Healthcare - A Preprint","Machine learning offers promise for personalized medicine, yet clinical adoption remains limited. A key barrier is sample selection bias (SSB), where the study population is less representative of the target population, causing biased and potentially harmful decisions. Prior work mainly balances distributions across study and target groups, which can reduce predictive performance. This preprint demonstrates SSB risks on algorithm performance and proposes target-population identification via T-Net and MT-Net.","arXiv :2405 .07841v1 [ cs .LG] 13 May 2024  \nSAMPLE SELECTION BIAS IN MACHINE LEARNING FOR  \nHEALTHCARE  \nA PREPRINT  \nVinod Kumar Chauhan 1  Lei Clifton 1 ,2 , Achille Salaün 1 , Huiqi Yvonne Lu 1 , Kim Branson3 , Patrick Schwab3 , Gaurav Nigam 1 ,4 , David A. Clifton 1 ,5  \n1Institute of Biomedical Engineering, University of Oxford, UK  \n2Nuffield Department of Population Health, University of Oxford, UK  \n3Biomedical AI Group, GSK  \n4Translational Gastroenterology Unit, Nuffield Department of Medicine, University of Oxford, Oxford, UK  \n5 Oxford-Suzhou Institute of Advanced Research (OSCAR), Suzhou, China  \nMay 14, 2024  \nABSTRACT  \nWhile machine learning algorithms hold promise for personalised medicine, their clinical adoption remains limited. One critical factor contributing to this restraint is sample selection bias (SSB) which refers to the study population being 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 techniques try to correct the bias 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 identification rather than the bias correction. Specifically, we propose two independent networks (T-Net) and a multitasking network (MT-Net) for addressing SSB, where one network/task identifies the target subpopulation which is representative of the study population and the second makes predictions for the identified subpopulation. Our empirical results with synthetic and semi-synthetic datasets highlight that SSB can lead to a large drop in the performance of an algorithm for the target population as compared with the study population, as well as a substantial difference in the performance for the target subpopulations that are representative of the selected and the non-selected patients from the study population. Furthermore, our proposed techniques demonstrate robustness across various settings, including different dataset sizes, event rates, and selection rates, outperforming the existing bias correction techniques.  \nKeywords Sample Selection Bias · Target Population · Machine Learning · Healthcare · Risk Prediction.  \n1 Introduction  \nMachine learning algorithms have demonstrated high diagnostic and prognostic accuracy in identifying and categorising diseases, promising personalised interventions and informed decision-making in healthcare [49, 58] . Their ability to analyse vast datasets and uncover hidden patterns surpasses traditional techniques and sometimes even human experts in task-specific applications, such as image-recognition tasks in radiology [36, 33] . However, despite their compelling  \n∗ Corresponding author: Vinod Kumar Chauhan ([vinod.kumar@eng.ox.ac.uk](vinod.kumar@eng.ox.ac.uk))  \npotential and an increasing number of studies every year, their clinical adoption remains constrained by various issues [61] . One such issue is sample selection bias (SSB), recognised as a fundamental pitfall in the research design of clinical studies [65] but remains scarcely studied in machine learning for healthcare, posing a potential hurdle to its real-world applications.  \nInherent in clinical studies, which serve as a source of data for machine learning, is the practice of sample selection, characterised by strict criteria for the inclusion and exclusion of patients [45] . However, these studies don’t add any bias as long as the study population is representative of the target population. The study population is defined as data used to develop the model and generally split into training,(internal) ","cbCaibTmb9MHUqJ2","https://ap.wps.com/l/cbCaibTmb9MHUqJ2","pdf",1124224,1,20,"English","en",105,"# Abstract\n# Introduction\n## Sample selection and study vs target populations\n## SSB mechanisms and consequences\n# (Figure) Illustration of SSB impact","[{\"question\":\"What is sample selection bias (SSB) in this context?\",\"answer\":\"SSB occurs when the study population is selected through a non-uniform process, making its distribution differ from the target population used for deployment or external validation.\"},{\"question\":\"Why can common bias-correction methods hurt performance?\",\"answer\":\"Techniques that correct SSB by balancing distributions between study and target populations may lead to a loss of predictive performance.\"},{\"question\":\"What approach do the authors propose to address SSB?\",\"answer\":\"The study proposes target population identification rather than bias correction, using two independent networks (T-Net) and a multitasking network (MT-Net) to identify representative subpopulations and then predict for them.\"}]","Sample Selection Bias in Machine Learning for Healthcare - A Preprint | PDF",1785721633,50,{"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-a-preprint","",{"@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-a-preprint/118999/",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","2026-08-03",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 this context?","Question",{"text":75,"@type":76},"SSB occurs when the study population is selected through a non-uniform process, making its distribution differ from the target population used for deployment or external validation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why can common bias-correction methods hurt performance?",{"text":80,"@type":76},"Techniques that correct SSB by balancing distributions between study and target populations may lead to a loss of predictive performance.",{"name":82,"@type":73,"acceptedAnswer":83},"What approach do the authors propose to address SSB?",{"text":84,"@type":76},"The study proposes target population identification rather than bias correction, using two independent networks (T-Net) and a multitasking network (MT-Net) to identify representative subpopulations and then predict for them.","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},[93,97,101,105,110,114,119,122,126,129,133],{"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":29,"slug":113},6,"Technology","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":21,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":21,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]