[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121574-en":3,"doc-seo-121574-105":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},121574,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",7,"Healthcare","Interpretable multimodal machine learning model for predicting health risks of patients with heart failure - read and summarize key findings","Heart failure (HF) is a major global cause of morbidity and mortality, demanding accurate tools for predicting outcomes and stratifying risk. This study presents an interpretable multimodal machine learning framework that integrates four clinical data modalities—demographics, medications, laboratory tests, and electrocardiograms (ECGs)—to forecast 30-day all-cause mortality and hospital readmission. Using data from 2,868 HF patients across 43 Hong Kong hospitals, ten models are trained and compared, achieving strong discrimination. Laboratory tests and ECG features drive performance, while SHAP highlights key biomarkers and QT-related measures.","King’s Research Portal  \nDOI:  \n10.1016/j.ymeth.2026.02.007  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication record in King's Research Portal  \nCitation for published version (APA):  \nChae, R. , Zhou, J. , Chou, O. H. I. , Yang, B. , Pu, H. , Tse, G. , Cheung, B. M. Y. , Zhu, T. , Car, J. , & Lu, L. (2026) . Interpretable multimodal machine learning model for predicting health risks of patients with heart failure. Methods, 249, 23-36 . Advance online publication. [https://doi.org/10.1016/j.ymeth.2026.02.007](https://doi.org/10.1016/j.ymeth.2026.02.007)  \nCiting this paper  \nPlease note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination, volume/issue, and date of publication details. And where the final published version is provided on the Research Portal, if citing you are again advised to check the publisher's website for any subsequent corrections.  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the Research Portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognize and abide by the legal requirements associated with these rights.  \n•Users may download and print one copy of any publication from the Research Portal for the purpose of private study or research.  \n•You may not further distribute the material or use it for any profit-making activity or commercial gain  \n•You may freely distribute the URL identifying the publication in the Research Portal  \nTake down policy  \nIf you believe that this document breaches copyright please contact [librarypure@kcl.ac.uk](librarypure@kcl.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 09. Mar. 2026  \nMethods 249 (2026) 23–36  \nContents lists available at ScienceDirect  \nMethods  \njournal [homepage: www. elsevier. com/locate/y meth](homepage: www. elsevier. com/locate/y meth)  \n| Interpretable multimodal machine learning model for predicting health risks of patients with heart failure |  |  |  |\n| --- | --- | --- | --- |\n| Rachel Chaea,1 , Jiandong Zhou b, c, d,1 , Oscar Hou In Chou e, Bingqing Yang b, Haolin Pu f, Gary Tseg, h, Bernard Man Yung Cheung e, Tingting Zhu i, Josip Carj, Lei Luj,∗ iD\u003Cbr>a Nuffield Department of Primary Care Health Sciences, Somerville College, University of Oxford, Oxford, United Kingdom\u003Cbr>b Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region of China\u003Cbr>c School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region of China d Department of Pharmacology and Pharmacy, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region of China\u003Cbr>e Division of Clinical Pharmacology, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region of China\u003Cbr>f Department of Applied Science, School of Science and Technology, Hong Kong Metropolitan University, Hong Kong Special Administrative Region of China g Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin 300211, China\u003Cbr>h Department of Health Sciences, School of Nursing and Health Studies, Hong Kong Metropolitan University, Hong Kong Special Administrative Region of China i Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom\u003Cbr>j School of Life Course & Population Sciences, King’s C","cbCaioWfsLNKVYSm","https://ap.wps.com/l/cbCaioWfsLNKVYSm","pdf",5702802,1,15,"English","en",105,"# Highlights\n## Interpretable multimodal approach\n## Evaluation of multiple model configurations\n# Abstract\n## Study aim and data sources\n## Model performance and key predictors\n## Explainability and feature redundancy","[{\"question\":\"What outcomes does the proposed model predict in heart failure patients?\",\"answer\":\"It predicts 30-day all-cause mortality and hospital readmission using multiple clinical data modalities.\"},{\"question\":\"Which data modalities are integrated into the interpretable model?\",\"answer\":\"The model combines demographics, medications, laboratory tests, and electrocardiograms (ECGs).\"},{\"question\":\"How is model interpretability assessed, and what key predictors are identified?\",\"answer\":\"SHAP analysis is used to interpret predictions, highlighting serum albumin, high-sensitivity troponin I, lactate dehydrogenase, and QT interval dispersion as key predictors.\"}]","Interpretable multimodal machine learning model for predicting health risks of patients with heart failure - read and summarize key findings | PDF",1785736307,38,{"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},"interpretable-multimodal-machine-learning-model-for-predicting-health-risks-of-patients-with-heart-failure-read-and-summarize-key-findings","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/interpretable-multimodal-machine-learning-model-for-predicting-health-risks-of-patients-with-heart-failure-read-and-summarize-key-findings/121574/",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 outcomes does the proposed model predict in heart failure patients?","Question",{"text":75,"@type":76},"It predicts 30-day all-cause mortality and hospital readmission using multiple clinical data modalities.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which data modalities are integrated into the interpretable model?",{"text":80,"@type":76},"The model combines demographics, medications, laboratory tests, and electrocardiograms (ECGs).",{"name":82,"@type":73,"acceptedAnswer":83},"How is model interpretability assessed, and what key predictors are identified?",{"text":84,"@type":76},"SHAP analysis is used to interpret predictions, highlighting serum albumin, high-sensitivity troponin I, lactate dehydrogenase, and QT interval dispersion as key predictors.","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},[93,97,101,105,110,115,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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"]