[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124662-en":3,"doc-seo-124662-105":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},124662,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Development and validation of machine learning models in cardiology - Thesis","Development and Validation of Machine Learning Models in Cardiology studies how machine learning can improve prognostic and diagnostic decision support for cardiovascular disease. The thesis introduces and evaluates model development strategies aimed at better prediction across clinical settings, focusing on TAVI outcomes, atrial fibrillation recurrence after ablation, coronary computed tomography angiography contrast adequacy, and electrocardiogram-based detection of rare genetic heart disease. Emphasis is placed on temporal validation, local and distributed learning, and approaches that reduce the need for patient data sharing while maintaining performance.","UvA-DARE (Digital Academic Repository)  \nDevelopment and validation of machine learning models in cardiology  \nRicci Lopes, R.  \nPublication date  \n2023  \nDocument Version  \nFinal published version  \nLink to publication  \nCitation for published version (APA):  \nRicci Lopes, R. (2023) . Development and validation of machine learning models in cardiology.[Thesis, fully internal, Universiteit van Amsterdam] .  \nGeneral rights  \nIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons) .  \nDisclaimer/Complaints regulations  \nIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: [https://uba.uva.nl/en/contact](https://uba.uva.nl/en/contact), or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.  \nUvA-DARE is a service provided by the library of the University of Amsterdam ( [http](https://dare. uva. nl)[s](https://dare. uva. nl)[://dare. uva. nl](https://dare. uva. nl))  \nDownload date:15 Apr 2023  \nDevelopment and Validation of Machine Learning Models in Cardiology  \nRicardo Ricci Lopes  \nLayout: Ricardo Ricci Lopes  \nCover Design: Jonas Marques  \n[Print: Ridderprint | www.ridderprint.nl](Print: Ridderprint | www.ridderprint.nl)  \n[ISBN: 978-94-6483-014-9](ISBN: 978-94-6483-014-9)  \nThe work in this thesis was supported by ITEA3 Partner: Project 16017.  \nFinancial support by the Dutch Heart Foundation for publication of this thesis is gratefully acknowledged.  \nCopyright © R.R. Lopes 2023. All rights are reserved. No part of this book may be reproduced, distributed, stored in a retrieval system, or transmitted in any form or by any means, without prior written permission of the author.  \nDevelopment and Validation of Machine Learning Models in Cardiology  \nACADEMISCH PROEFSCHRIFT  \nter verkrijging van de graad van doctor  \naan de Universiteit van Amsterdam  \nop gezag van de Rector Magnificus  \n[prof. dr. ir. P.P.C.C. Verbeek](prof. dr. ir. P.P.C.C. Verbeek)  \nten overstaan van een door het College voor Promoties ingestelde commissie, in het openbaar te verdedigen in de Agnietenkapel op woensdag 19 april 2023, te 10.00 uur  \ndoor Ricardo Ricci Lopes  \ngeboren te Batatais/SP  \nPromotiecommissie  \nPromotores:  \nOverige leden:  \nprof. dr. H.A. Marquering [prof. mr. dr. B.A.J.M. de Mol](prof. mr. dr. B.A.J.M. de Mol)  \n[prof. dr. I. I](prof. dr. I. I)š[gum](gum)[ ](gum)[prof. dr. M.C. Schut](prof. dr. M.C. Schut)[ ](prof. dr. M.C. Schut)[prof. dr. J. Kluin](prof. dr. J. Kluin)[ ](prof. dr. J. Kluin)prof. dr. A. Abu-Hanna prof. dr. R.J. de Winter [prof. dr. ir. H. Boersma](prof. dr. ir. H. Boersma)  \nAMC-UvA  \nAMC-UvA  \nAMC-UvA  \nVrije Universiteit Amsterdam AMC-UvA  \nAMC-UvA  \nAMC-UvA  \nErasmus Universiteit Rotterdam  \nFaculteit der Geneeskunde  \nTable of Contents  \nChapter 1  \nIntroduction  \nChapter 2  \nValue of machine learning in predicting TAVI outcomes  \nChapter 3  \nInter-center cross-validation and finetuning without patient data sharing for predicting transcatheter aortic valve implantation outcome  \nChapter 4  \nLocal and distributed machine learning for inter-hospital data utilization: an application for TAVI outcome prediction  \nChapter 5  \nTemporal validation of 30-day mortality prediction models for transcatheter aortic valve implantation using statistical process control  \nChapter 6  \nPrediction of atrial fibrillation recurrence after thoracoscopic surgical ablation using machine learning techniques  \nChapter 7  \nMachine learning-based prediction of insufficient cont","cbCaihSM42iYnKZl","https://ap.wps.com/l/cbCaihSM42iYnKZl","pdf",20055024,1,213,"English","en",105,"# Introduction\n# Value of machine learning in predicting TAVI outcomes\n# Inter-center cross-validation and finetuning without patient data sharing for predicting transcatheter aortic valve implantation outcome\n# Local and distributed machine learning for inter-hospital data utilization: an application for TAVI outcome prediction\n# Temporal validation of 30-day mortality prediction models for transcatheter aortic valve implantation using statistical process control\n# Prediction of atrial fibrillation recurrence after thoracoscopic surgical ablation using machine learning techniques\n# Machine learning-based prediction of insufficient contrast enhancement in coronary computed tomography angiography\n# Improving electrocardiogram-based detection of rare genetic heart disease using transfer learning\n# Discussion Summary","[{\"question\":\"Why are machine learning models considered for cardiology decision support?\",\"answer\":\"Traditional prognostic risk scores may use outdated statistical methods and can show variable accuracy across populations. Machine learning methods can improve prediction accuracy and support clinicians and patients in treatment decisions.\"},{\"question\":\"How does the thesis address data sharing limitations between centers?\",\"answer\":\"It examines inter-center cross-validation and finetuning methods that avoid patient data sharing, and it explores local and distributed machine learning for inter-hospital data utilization for TAVI outcome prediction.\"},{\"question\":\"What validation aspects are emphasized for prediction models?\",\"answer\":\"The work includes temporal validation of 30-day mortality prediction models using statistical process control, as well as discussion of validation performance across different settings.\"}]","Development and validation of machine learning models in cardiology - Thesis | PDF",1785893628,537,{"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},"development-and-validation-of-machine-learning-models-in-cardiology-thesis","",{"@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/development-and-validation-of-machine-learning-models-in-cardiology-thesis/124662/",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","2026-08-05",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},"Why are machine learning models considered for cardiology decision support?","Question",{"text":75,"@type":76},"Traditional prognostic risk scores may use outdated statistical methods and can show variable accuracy across populations. Machine learning methods can improve prediction accuracy and support clinicians and patients in treatment decisions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis address data sharing limitations between centers?",{"text":80,"@type":76},"It examines inter-center cross-validation and finetuning methods that avoid patient data sharing, and it explores local and distributed machine learning for inter-hospital data utilization for TAVI outcome prediction.",{"name":82,"@type":73,"acceptedAnswer":83},"What validation aspects are emphasized for prediction models?",{"text":84,"@type":76},"The work includes temporal validation of 30-day mortality prediction models using statistical process control, as well as discussion of validation performance across different settings.","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"]