[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125443-en":3,"doc-seo-125443-105":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},125443,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Development and validation of machine learning models in cardiology - Thesis","Research on cardiology prediction models centers on developing and validating machine learning approaches to improve prognostic and diagnostic decision support. The work addresses limitations of existing traditional risk scores by updating predictive performance over time and across populations. It covers strategies such as inter-center validation and fine-tuning, local and distributed learning to enable data utilization without patient data sharing, and temporal evaluation of mortality and outcome predictions. Additional applications include atrial fibrillation recurrence, contrast enhancement insufficiency in coronary CT angiography, and rare genetic heart disease detection using transfer learning.","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:05 Jan 2026  \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","cbCaih5TJfayFSwd","https://ap.wps.com/l/cbCaih5TJfayFSwd","pdf",20055805,1,213,"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 research in this thesis?\",\"answer\":\"To develop and validate machine learning models for cardiology to improve prediction and support clinical decision-making.\"},{\"question\":\"How does the thesis handle data sharing limitations between centers?\",\"answer\":\"It applies inter-center validation and fine-tuning approaches designed to avoid patient data sharing, and it explores local and distributed learning for hospital data utilization.\"},{\"question\":\"Which cardiology outcomes and tasks are modeled using machine learning?\",\"answer\":\"The work includes TAVI-related outcome prediction, temporal 30-day mortality prediction, atrial fibrillation recurrence after ablation, contrast enhancement insufficiency in coronary CT angiography, and rare genetic heart disease detection from electrocardiograms.\"}]","Development and validation of machine learning models in cardiology - 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