[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117889-en":3,"doc-seo-117889-105":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},117889,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","AutoPrognosis 2.0 - Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning","Diagnostic and prognostic models play an expanding role in clinical decisions, yet machine-learning methods face technical and practical barriers that limit routine adoption in healthcare settings. AutoPrognosis 2.0 is an open-source machine-learning framework designed to ease model development and deployment. It automates optimized diagnostic and prognostic pipeline construction via automated machine learning, integrates model explainability tools, and supports clinical demonstrators without advanced technical expertise. An illustrative UK Biobank diabetes application yields better discrimination than expert clinical risk scores and is provided as a web-based decision support tool.","PLOS DIGITAL HEALTH  \nOPEN ACCESS  \nCitation: Imrie F, Cebere B, McKinney EF, van der Schaar M (2023) AutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning. PLOS Digit Health 2(6): e0000276 . [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.1371/journal.pdig.0000276](10.1371/journal.pdig.0000276)  \nEditor: Gilles Guillot, CSL Behring / Swiss Institute for Translational and Entrepreneurial Medicine (SITEM), SWITZERLAND  \nReceived: November 21, 2022  \nAccepted: May 17, 2023  \nPublished: June 22, 2023  \nCopyright: © 2023 Imrie et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData Availability Statement: This research has been conducted using the UK Biobank resource. Data from UK Biobank is accessible through a request process ([https://www.ukbiobank.ac.uk/](https://www.ukbiobank.ac.uk/)[ ](https://www.ukbiobank.ac.uk/)[enable-your-research/register](enable-your-research/register)) . The authors had no special access or privileges when accessing the data.  \nFunding: The authors received no specific funding for this work.  \nRESEARCH ARTICLE  \nAutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning  \nFergus Imrie1 *, Bogdan Cebere2, Eoin F. McKinney3, Mihaelavan der Schaar2,4  \n1 Department of Electrical and Computer Engineering, University of California, Los Angeles, California, United States of America, 2 Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom, 3 Department of Medicine, University of Cambridge, Cambridge, United Kingdom, 4 The Alan Turing Institute, London, United Kingdom  \n* [imrie@ucla.edu](imrie@ucla.edu)  \nAbstract  \nDiagnostic and prognostic models are increasingly important in medicine and inform many clinical decisions. Recently, machine learning approaches have shown improvement over conventional modeling techniques by better capturing complex interactions between patient covariates in a data-driven manner. However, the use of machine learning introduces technical and practical challenges that have thus far restricted widespread adoption of such techniques in clinical settings. To address these challenges and empower healthcare professionals, we present an open-source machine learning framework , AutoPrognosis 2 .0, to facilitate the development of diagnostic and prognostic models. AutoPrognosis leverages state-of-the-art advances in automated machine learning to develop optimized machine learning pipelines, incorporates model explainability tools, and enables deployment of clinical demonstrators, without requiring significant technical expertise. To demonstrate AutoPrognosis 2 .0, we provide an illustrative application where we construct a prognostic risk score for diabetes using the UK Biobank, a prospective study of 502,467 individuals. The models produced by our automated framework achieve greater discrimination for diabetes than expert clinical risk scores. We have implemented our risk score as a web-based decision support tool, which can be publicly accessed by patients and clinicians. By open-sourcing our framework as a tool for the community, we aim to provide clinicians and other medical practitioners with an accessible resource to develop new risk scores, personalized diagnostics, and prognostics using machine learning techniques.  \nSoftware: [https://github.com/vanderschaarlab/AutoPrognosis](https://github.com/vanderschaarlab/AutoPrognosis)  \nAuthor summary  \nPrevious studies have reported promising applications of machine learning (ML) approaches in healthcare. However, there remain significant challenges to using ML for diagnostic and prognostic modeling, particularly for non-ML experts, that currently prevent broader ad","cbCaijZkGMuwEqdp","https://ap.wps.com/l/cbCaijZkGMuwEqdp","pdf",1842235,1,21,"English","en",105,"# Abstract\n# Introduction\n## Machine learning challenges in healthcare\n## AutoPrognosis and automated machine learning overview","[{\"question\":\"What problem does AutoPrognosis 2.0 address in healthcare machine learning?\",\"answer\":\"It addresses technical and practical barriers that restrict widespread use of machine-learning approaches for diagnostic and prognostic modeling in clinical settings.\"},{\"question\":\"What capabilities are included in the AutoPrognosis 2.0 framework?\",\"answer\":\"It provides automated machine-learning pipeline optimization, model explainability tools, and support for deployment of clinical demonstrators without requiring significant technical expertise.\"},{\"question\":\"How was AutoPrognosis 2.0 evaluated in the document?\",\"answer\":\"The document presents a diabetes prognostic risk-score example using UK Biobank data and reports improved discrimination versus expert clinical risk scores, implemented as a web-based decision support tool.\"}]","AutoPrognosis 2.0 - Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning | PDF",1785680186,53,{"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},"autoprognosis-20-democratizing-diagnostic-and-prognostic-modeling-in-healthcare-with-automated-machine-learning","",{"@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/autoprognosis-20-democratizing-diagnostic-and-prognostic-modeling-in-healthcare-with-automated-machine-learning/117889/",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 problem does AutoPrognosis 2.0 address in healthcare machine learning?","Question",{"text":75,"@type":76},"It addresses technical and practical barriers that restrict widespread use of machine-learning approaches for diagnostic and prognostic modeling in clinical settings.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What capabilities are included in the AutoPrognosis 2.0 framework?",{"text":80,"@type":76},"It provides automated machine-learning pipeline optimization, model explainability tools, and support for deployment of clinical demonstrators without requiring significant technical expertise.",{"name":82,"@type":73,"acceptedAnswer":83},"How was AutoPrognosis 2.0 evaluated in the document?",{"text":84,"@type":76},"The document presents a diabetes prognostic risk-score example using UK Biobank data and reports improved discrimination versus expert clinical risk scores, implemented as a web-based decision support tool.","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"]