[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117939-en":3,"doc-seo-117939-105":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},117939,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","AutoPrognosis 2.0 - Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning","Diagnostic and prognostic models increasingly shape medical decision-making, and machine learning improves performance by learning complex relationships in patient data. Yet adoption in clinical practice is limited by technical and practical barriers. AutoPrognosis 2.0 is presented as an open-source automated machine learning framework that streamlines development of diagnostic and prognostic pipelines, adds model explainability, and supports deployment of clinical demonstrators without requiring substantial expertise. An application builds a diabetes prognostic risk score from UK Biobank, achieving stronger discrimination than expert clinical risk scores and delivering it via a public web-based decision support tool. By open-sourcing the framework, the work enables clinicians and other practitioners to create accessible, personalized diagnostics and prognostics.","UCLA  \nUCLA Previously Published Works  \nTitle  \nAutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning  \nPermalink  \n[https://escholarship.org/uc/item/8419m689](https://escholarship.org/uc/item/8419m689)  \nJournal  \nPLOS Digital Health, 2(6)  \nISSN  \n2767-3170  \nAuthors  \nImrie, Fergus  \nCebere, Bogdan McKinney, Eoin Fet al.  \nPublication Date  \n2023  \nDOI  \n10.1371/journal.pdig.0000276  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nPLOS 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","cbCaivNqBY6b9EAG","https://ap.wps.com/l/cbCaivNqBY6b9EAG","pdf",1960949,1,22,"English","en",105,"# Abstract\n# Introduction\n## Machine learning in medicine\n# Author summary\n# References and links","[{\"question\":\"What is AutoPrognosis 2.0 and what problem does it target?\",\"answer\":\"AutoPrognosis 2.0 is an open-source automated machine learning framework designed to make it easier to develop diagnostic and prognostic models in healthcare despite technical and practical barriers that slow adoption.\"},{\"question\":\"What capabilities does AutoPrognosis 2.0 provide for model development and use?\",\"answer\":\"It configures and optimizes machine learning pipelines via automated machine learning, incorporates model explainability tools, and enables deployment of clinical demonstrators without requiring significant technical expertise.\"},{\"question\":\"How was AutoPrognosis 2.0 evaluated in the document?\",\"answer\":\"The document demonstrates it by constructing a diabetes prognostic risk score using UK Biobank data for 502,467 individuals, and reports improved discrimination compared with expert clinical risk scores, implemented as a web-based decision support tool.\"}]","AutoPrognosis 2.0 - 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