[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127499-en":3,"doc-seo-127499-105":31,"detail-sidebar-cat-0-en-105":96},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127499,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",7,"Healthcare","External validity of machine learning-based prognostic scores for cystic fibrosis: A retrospective study using the UK and Canadian registries","Precise and timely referral for lung transplantation is critical for survival in terminal cystic fibrosis. Machine learning models can improve prognostic accuracy, but external validity and resulting referral policies remain insufficiently studied. This retrospective study evaluates ML-based prognostic models using annual follow-up data from UK and Canadian Cystic Fibrosis registries, assessing effects of population characteristics and clinical practice differences. External validation shows reduced accuracy overall yet improved prognostic power after accounting for subgroup variations, underscoring the need for cross-population validation and potential transfer learning.","PLOS DIGITAL HEALTH  \nOPEN ACCESS  \nCitation: Qin Y, Alaa A, Floto A, Schaar Mvd (2023) External validity of machine learning-based prognostic scores for cystic fibrosis: A retrospective study using the UK and Canadian registries. PLOS Digit Health 2(1): e0000179 .  \n[https://doi.org/10.1371/journal.pdig.0000179](https://doi.org/10.1371/journal.pdig.0000179)  \n[Editor:](Editor: Mecit Can Emre Simsekler)[ Mecit Can Emre Simsekler](Editor: Mecit Can Emre Simsekler), Khalifa University of Science and Technology, UNITED ARAB EMIRATES  \nReceived: August 12, 2022  \nAccepted: December 8, 2022  \nPublished: January 12, 2023  \nPeer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here:  \n[https://doi.org/10.1371/journal.pdig.0000179](https://doi.org/10.1371/journal.pdig.0000179)  \n[Copyright:](Copyright:) © [2023](2023) Qin 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: Third-party registry data was used in this study, and the authors cannot legally distribute the data due to participant  \nRESEARCH ARTICLE  \nExternal validity of machine learning-based prognostic scores for cystic fibrosis: A retrospective study using the UK and Canadian registries  \nYuchao Qin1 *, Ahmed Alaa2,3, Andres Floto1, Mihaela van der Schaar1,4,5  \n1 University of Cambridge, Cambridge, United Kingdom, 2 University of California Berkeley, Berkeley, California, United States of America, 3 University of California San Francisco, San Francisco, California, United States of America, 4 Alan Turing Institute, London, United Kingdom, 5 University of California Los Angeles, Los Angeles, California, United States of America  \n* [yq257@cam.ac.uk](yq257@cam.ac.uk)  \nAbstract  \nPrecise and timely referral for lung transplantation is critical for the survival of cystic fibrosis patients with terminal illness. While machine learning (ML) models have been shown to achieve significant improvement in prognostic accuracy over current referral guidelines, the external validity of these models and their resulting referral policies has not been fully investigated. Here, we studied the external validity of machine learning-based prognostic models using annual follow-up data from the UK and Canadian Cystic Fibrosis Registries. Using a state-of-the-art automated ML framework, we derived a model for predicting poor clinical outcomes in patients enrolled in the UK registry, and conducted external validation of the derived model using the Canadian Cystic Fibrosis Registry. In particular, we studied the effect of (1) natural variations in patient characteristics across populations and (2) differences in clinical practice on the external validity of ML-based prognostic scores. Overall, decrease in prognostic accuracy on the external validation set (AUCROC: 0.88, 95% CI 0.88-0.88) was observed compared to the internal validation accuracy (AUCROC: 0.91, 95% CI 0.90-0.92) . Based on our ML model, analysis on feature contributions and risk strata revealed that, while external validation of ML models exhibited high precision on average, both factors (1) and (2) can undermine the external validity of ML models in patient subgroups with moderate risk for poor outcomes. A significant boost in prognostic power (F1 score) from 0.33 (95% CI 0.31-0.35) to 0.45 (95% CI 0 .45-0.45) was observed in external validation when variations in these subgroups were accounted in our model. Our study highlighted the significance of external validation of ML models for cystic fibrosis prognostication. The uncovered insights on key risk factors and patient subgroups can be use","cbCaiivu9rvA2Ij0","https://ap.wps.com/l/cbCaiivu9rvA2Ij0","pdf",1478092,3,1,17,"English","en",105,"# Abstract\n## External validity and population/practice effects\n## Model derivation and external validation\n## Feature contributions and risk stratification\n## Implications for cross-population adaptation","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To assess the external validity of machine learning-based prognostic scores for cystic fibrosis using data from the UK and Canadian registries, focusing on how population and clinical practice differences affect performance.\"},{\"question\":\"How was the prognostic model developed and validated externally?\",\"answer\":\"A model was derived using annual follow-up data from the UK Cystic Fibrosis Registry and then externally validated on the Canadian Cystic Fibrosis Registry using an automated state-of-the-art ML framework.\"},{\"question\":\"What did the study find about external validation performance?\",\"answer\":\"Overall prognostic accuracy decreased in external validation compared with internal validation, but the model showed high precision on average and risk subgroup analyses indicated that population and practice differences can undermine performance in moderate-risk groups.\"},{\"question\":\"How did accounting for subgroup variations change prognostic power?\",\"answer\":\"When variations in moderate-risk subgroups were accounted for in the model, prognostic power increased significantly in external validation, with an observed F1 score improvement from 0.33 to 0.45.\"}]","External validity of machine learning-based prognostic scores for cystic fibrosis: A retrospective study using the UK and Canadian registries | 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is the main objective of the study?","Question",{"text":76,"@type":77},"To assess the external validity of machine learning-based prognostic scores for cystic fibrosis using data from the UK and Canadian registries, focusing on how population and clinical practice differences affect performance.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How was the prognostic model developed and validated externally?",{"text":81,"@type":77},"A model was derived using annual follow-up data from the UK Cystic Fibrosis Registry and then externally validated on the Canadian Cystic Fibrosis Registry using an automated state-of-the-art ML framework.",{"name":83,"@type":74,"acceptedAnswer":84},"What did the study find about external validation performance?",{"text":85,"@type":77},"Overall prognostic accuracy decreased in external validation compared with internal validation, but the model showed high precision on average and risk subgroup analyses indicated that population and practice differences can undermine performance in moderate-risk groups.",{"name":87,"@type":74,"acceptedAnswer":88},"How did accounting for subgroup variations change prognostic power?",{"text":89,"@type":77},"When variations in moderate-risk subgroups were accounted for in the model, prognostic power increased significantly in external validation, with an observed F1 score improvement from 0.33 to 0.45.","https://schema.org",{"og:url":52,"og:type":92,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":94,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":97},[98,102,106,110,115,120,123,128,133,136,140],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Story & 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