[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119029-en":3,"doc-seo-119029-105":30,"detail-sidebar-cat-0-en-105":92},{"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":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},119029,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Uncertainty in Machine Learning - A Safety Perspective on Biomedical Applications","Uncertainty is an inevitable feature of real-world decision-making and it directly affects machine learning models when they produce predictions under imperfect information. This work emphasizes uncertainty quantification as a core capability rather than an optional add-on, arguing that acknowledging uncertainty improves reliability and trustworthiness of systems operating in complex environments. The thesis develops and adapts uncertainty quantification methods, integrates them into the machine learning development pipeline, and applies them to clinical decision-making with classification and rejection option. Evaluation on physiological signal databases supports that the proposed approach yields more robust, interpretable models and safer prediction behavior.","DEPARTMENT OF PHYSICS  \nUNCERTAINTY IN MACHINE LEARNING A SAFETY PERSPECTIVE ON BIOMEDICAL APPLICATIONS  \nMARÍLIA DA SILVEIRA GOUVEIA BARANDAS  \nMaster in Biomedical Engineering  \nDOCTORATE IN BIOMEDICAL ENGINEERING  \nNOVA University Lisbon March, 2023  \nDEPARTMENT OF PHYSICS  \nUNCERTAINTY IN MACHINE LEARNING  \nA SAFETY PERSPECTIVE ON BIOMEDICAL APPLICATIONS  \nMARÍLIA DA SILVEIRA GOUVEIA BARANDAS  \nMaster in Biomedical Engineering  \nAdviser: Hugo Filipe Silveira Gamboa  \nAssociate Professor with Habilitation, NOVA School of Science and Technology  \nExamination Committee:  \nChair: Orlando Manuel Neves Duarte Teodoro  \nFull Professor, NOVA School of Science and Technology  \nRapporteurs: Eyke Hüllermeier  \nFull Professor, Ludwig-Maximilians-Universität München  \nAndré Ribeiro Lourenço  \nAssistant Professor, Instituto Superior de Engenharia de Lisboa  \nAdviser: Hugo Filipe Silveira Gamboa  \nAssociate Professor with Habilitation, NOVA School of Science and Technology  \nMembers: Ricardo Nuno Pereira Verga e Afonso Vigário  \nAssociate Professor with Habilitation, NOVA School of Science and Technology Orlando Manuel Neves Duarte Teodoro  \nFull Professor, NOVA School of Science and Technology  \nDOCTORATE IN BIOMEDICAL ENGINEERING  \nNOVA University Lisbon March, 2023  \nUncertainty in Machine Learning  \nCopyright © Marília da Silveira Gouveia Barandas, NOVA School of Science and Technology, NOVA University Lisbon.  \nThe NOVA School of Science and Technology and the NOVA University Lisbon have the right, perpetual and without geographical boundaries, to file and publish this dissertation through printed copies reproduced on paper or on digital form, or by any other means known or that may be invented, and to disseminate through scientific repositories and admit its copying and distribution for non-commercial, educational or research purposes, as long as credit is given to the author and editor.  \nAcknowledgements  \nI would like to express my sincere gratitude to those who have supported and guided me throughout my Ph.D. journey.  \nFirst and foremost, I am deeply grateful to my supervisor, Hugo Gamboa, for his invaluable guidance and unwavering availability. He has been pivotal to my academic development, consistently showing confidence in my abilities and fostering an environment that allows me to thrive. Moreover, I want to thank him for encouraging me to make this document more inspirational.  \nI would also like to extend my heartfelt appreciation to Fraunhofer Portugal for providing me with the incredible opportunity to pursue my Ph.D. Their unwavering support and readiness to help whenever needed have been invaluable. A special thank you goes to Liliana Ferreira and Inês Sousa for making this opportunity possible.  \nMy journey would not have been the same without Duarte Folgado, who shared this experience with me. I am grateful for our scientific discussions, his unwavering support, and the inspiration he lent me throughout our time together. The memories and research achievements we have shared have made this research experience truly unforgettable.  \nI am also indebted to my lab colleagues at Fraunhofer for their stimulating research discussions and the fantastic work environment they helped create. Similarly, I would like to express my gratitude to MUDILab in Milan for their warm welcome and support during a crucial period of my Ph.D.  \nTo my dear friend Bárbara Viotty, I cannot thank you enough for always being there. Your motivational quote, \"A verdadeira viagem de descobrimento não consiste em procurar novas paisagens, mas em ter novos olhos (Marcel Proust)\", deserves to be documented in this document.  \nLast but not least, I would like to express my love and appreciation to my family, whose unwavering support has been a constant source of strength throughout this journey.  \n“All models are wrong, but some are useful .”(George Box)  \nAbstract  \nUncertainty is an inevitable and essential aspect of the world we live in and a funda","cbCailLkSyp1XCXP","https://ap.wps.com/l/cbCailLkSyp1XCXP","pdf",15300743,1,186,"English","en",105,"# Acknowledgements\n# Abstract\n# Uncertainty in Machine Learning\n## Safety perspective on biomedical applications","[{\"question\":\"Why is uncertainty quantification important in machine learning models?\",\"answer\":\"Uncertainty affects prediction quality and human-like decision-making requires accounting for uncertainty. Incorporating it enables more reliable, trustworthy systems that better handle real-world complexity, including clinical contexts.\"},{\"question\":\"What does the thesis focus on regarding uncertainty quantification?\",\"answer\":\"It covers developing and adapting uncertainty quantification methods, integrating them into the machine learning development pipeline, and applying them to clinical decision-making.\"},{\"question\":\"How do the proposed models handle uncertainty during prediction?\",\"answer\":\"They include abstaining capabilities, allowing the system to accept or reject predictions based on uncertainty level. This supports using classification with a rejection option in clinical decision support systems.\"}]","Uncertainty in Machine Learning - A Safety Perspective on Biomedical Applications | PDF",1785722000,469,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"uncertainty-in-machine-learning-a-safety-perspective-on-biomedical-applications","",{"@graph":36,"@context":86},[37,54,69],{"@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/uncertainty-in-machine-learning-a-safety-perspective-on-biomedical-applications/119029/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is uncertainty quantification important in machine learning models?","Question",{"text":76,"@type":77},"Uncertainty affects prediction quality and human-like decision-making requires accounting for uncertainty. Incorporating it enables more reliable, trustworthy systems that better handle real-world complexity, including clinical contexts.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What does the thesis focus on regarding uncertainty quantification?",{"text":81,"@type":77},"It covers developing and adapting uncertainty quantification methods, integrating them into the machine learning development pipeline, and applying them to clinical decision-making.",{"name":83,"@type":74,"acceptedAnswer":84},"How do the proposed models handle uncertainty during prediction?",{"text":85,"@type":77},"They include abstaining capabilities, allowing the system to accept or reject predictions based on uncertainty level. 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