[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121063-en":3,"doc-seo-121063-105":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},121063,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Data-Driven Human Decision Augmentation with Machine Learning","Increasingly available observational data is driving the push toward more personalized decision-making in medicine and healthcare, yet human analysis faces major obstacles from dataset scale and high dimensionality. This dissertation advances decision augmentation systems that combine expert knowledge, optimal decision strategies, and predictive evidence from observational datasets to support informed human choices. It studies four core problems: extracting expert knowledge, finding timely sensing and decision strategies, discovering evidence for personalized decisions, and evaluating cross-domain validity of models and strategies. Using quantitative epistemology, reinforcement learning, predictive clustering, and automated machine learning, the work introduces new formulations, models, and algorithms with experimental validation.","Data-Driven Human Decision  \nAugmentation with Machine Learning  \nYuchao Qin  \nDepartment of Applied Mathematics and Theoretical Physics University of Cambridge  \nThis thesis is submitted for the degree of Doctor of Philosophy  \nRobinson College July 2024  \nDeclaration  \nThis thesis is the result of my own work and includes nothing which is the outcome of work done in collaboration except as declared in the preface and specified in the text. It is not substantially the same as any work that has already been submitted, or, is being concurrently submitted, for any degree, diploma or other qualification at the University of Cambridge or anyother University or similar institution except as declared in the preface and specified in the text. It does not exceed the prescribed word limit for the relevant Degree Committee.  \nYuchao Qin July 2024  \nAbstract  \nThe increasing availability of observational data has fuelled efforts to enable more personalized decision-making in medicine and healthcare. However, the large volumes and high dimensionality of these datasets present significant challenges for human analysis and evaluation. To address these challenges, the development of advanced decision augmentation systems is imperative. These systems empower humans to make informed decisions by integrating expert knowledge, optimal decision strategies, and predictive evidence of potential outcomes derived from observational datasets. To shed new light on the study of data-driven decision augmentation, this dissertation identifies and investigates four fundamental problems in this area: 1) extraction of expert knowledge from observational data, 2) search for timely and effective decision and sensing strategies, 3) discovery of evidence and insights to inform personalized decision-making, and 4) assessment of cross-domain validity of predictive models and decision strategies. Leveraging theories and techniques in quantitative epistemology, reinforcement learning, predictive clustering, and automated machine learning, we introduce new mathematical formulations, develop novel machine learning models and algorithms, and provide experimental evaluations to demonstrate the practical utility of our proposed solutions to these problems. Specifically, for expert knowledge extraction, we formalize the desiderata for understanding clinical decision-making using a case study on organ transplantation and propose a data-driven framework to identify key risk factors affecting decisions on organ offers in an individualized manner. For the search of effective sensing strategies, we introduce a novel risk-averse formulation of the active sensing task to tackle the continuous-time decision-making problem for longitudinal patient follow-ups. To facilitate evidence-based decision support using longitudinal observations, we develop a predictive clustering algorithm to discover patient phenotypes that associate unique temporal patterns in patient covariates with typical outcomes using frequency domain representations. Finally, to highlight the significance of cross-domain validity of machine learning models in decision augmentation, we provide a detailed analysis with registry data from cystic fibrosis patients of different demographics. By exploring these four fundamental perspectives, this dissertation effectively advances the frontier of data-driven decision augmentation and offers valuable insights for future research in this field.  \nAcknowledgements  \nI am deeply grateful to my supervisor, Professor Mihaela van der Schaar, for her exceptional guidance, unwavering support, and immense patience throughout my PhD journey. Her passion for research, visionary outlook, and courage to explore new fields have always inspired me, enabling me to navigate challenges in life and research and ultimately culminate in the completion of this dissertation. I am also indebted to Fergus Imrie, whose mentorship and friendship were invaluable during the early stages of my study. 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