[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125679-en":3,"doc-seo-125679-105":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},125679,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Development and validation of machine learning models in cardiology","The thesis investigates how machine learning models can improve prediction and diagnosis in cardiology, focusing on clinically relevant outcomes and model reliability. It addresses limitations of traditional risk scores by evaluating performance across time, institutions, and validation settings. Special emphasis is placed on approaches that enable learning while avoiding patient data sharing, including inter-center cross-validation, distributed and local training, and finetuning strategies. The work also explores transfer learning to enhance electrocardiogram-based rare genetic heart disease detection and evaluates statistical process control for mortality prediction.","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:31 Aug 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","cbCaikArg8fz36mo","https://ap.wps.com/l/cbCaikArg8fz36mo","pdf",20054995,1,213,"English","en",105,"# Chapter 1 Introduction\n# Chapter 2 Value of machine learning in predicting TAVI outcomes\n# Chapter 3 Inter-center cross-validation and finetuning without patient data sharing for predicting transcatheter aortic valve implantation outcome\n# Chapter 4 Local and distributed machine learning for inter-hospital data utilization: an application for TAVI outcome prediction\n# Chapter 5 Temporal validation of 30-day mortality prediction models for transcatheter aortic valve implantation using statistical process control\n# Chapter 6 Prediction of atrial fibrillation recurrence after thoracoscopic surgical ablation using machine learning techniques\n# Chapter 7 Machine learning-based prediction of insufficient contrast enhancement in coronary computed tomography angiography\n# Chapter 8 Improving electrocardiogram-based detection of rare genetic heart disease using transfer learning\n# Chapter 9 Discussion Summary","[{\"question\":\"What is the main goal of the thesis in cardiology?\",\"answer\":\"The thesis aims to develop and validate machine learning models for clinically important prediction and diagnostic tasks in cardiology, improving upon limitations of traditional statistical risk scores.\"},{\"question\":\"How does the thesis handle validation across different time periods and settings?\",\"answer\":\"It includes temporal validation of mortality prediction models and evaluates model performance in validation settings that reflect real-world changes, such as across institutions and over time.\"},{\"question\":\"Does the thesis address the issue of patient data sharing?\",\"answer\":\"Yes. It studies inter-center cross-validation and finetuning approaches designed to predict outcomes without patient data sharing, and also considers local and distributed learning for hospital data utilization.\"}]","Development and validation of machine learning models in cardiology | PDF",1785900612,537,{"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","",{"@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/125679/",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},"What is the main goal of the thesis in cardiology?","Question",{"text":75,"@type":76},"The thesis aims to develop and validate machine learning models for clinically important prediction and diagnostic tasks in cardiology, improving upon limitations of traditional statistical risk scores.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis handle validation across different time periods and settings?",{"text":80,"@type":76},"It includes temporal validation of mortality prediction models and evaluates model performance in validation settings that reflect real-world changes, such as across institutions and over time.",{"name":82,"@type":73,"acceptedAnswer":83},"Does the thesis address the issue of patient data sharing?",{"text":84,"@type":76},"Yes. It studies inter-center cross-validation and finetuning approaches designed to predict outcomes without patient data sharing, and also considers local and distributed learning for hospital data utilization.","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"]