[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117620-en":3,"doc-seo-117620-105":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},117620,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Algorithmic Fairness and Bias Mitigation in Clinical Machine Learning for Equitable Patient Outcomes - Doctoral Thesis","Recent years have seen machine learning models integrated into clinical settings to improve healthcare outcomes, yet fairness and equity concerns have risen—especially where biased algorithms can amplify existing disparities. This thesis examines fairness-aware approaches within clinical machine learning by analyzing how bias emerges and how it affects predictive performance across diverse patient populations. Using datasets from multiple healthcare institutions, it proposes and evaluates fairness-mitigation techniques to support equitable outcomes. The work contributes practical guidance and ethical recommendations for building socially responsible clinical AI.","Algorithmic Fairness and Bias Mitigation in Clinical Machine Learning for Equitable Patient  \nOutcomes  \nJenny Yang  \nBalliol College  \nUniversity of Oxford  \nThis thesis is submitted for the degree of Doctor of Philosophy  \nMichaelmas 2024  \nAbstract  \nIn recent years, the integration of machine learning algorithms into clinical settings has shown immense potential for improving healthcare outcomes. However, concerns regarding fairness and equity in machine learning models have garnered increasing attention, particularly in healthcare where biased algorithms can perpetuate existing disparities. This thesis investigates the role of fairness-aware algorithms in addressing these issues within clinical machine learning applications. Through case studies and empirical analyses, this research explores how biases manifest and impact model performance across diverse patient populations, highlighting the challenges and opportunities in promoting fairness within clinical machine learning. Subsequently, drawing on datasets from multiple healthcare institutions, we propose and assess the effectiveness of fairness-aware techniques in advancing equitable healthcare outcomes. Ultimately, this thesis contributes to the ongoing dialogue on fairness in machine learning, providing insights and recommendations for the development of ethically sound and socially responsible machine learning algorithms in healthcare.  \nAlgorithmic Fairness and Bias Mitigation in Clinical Machine Learning for Equitable Patient Outcomes  \nJenny Yang  \nBalliol College University of Oxford  \nThis thesis is submitted for the degree of Doctor of Philosophy  \nMichaelmas 2024  \nThis thesis is dedicated to my exceptional parents, Hui Zhang & Zhi Yong Yang, who have made so many sacrifices, and have nurtured in mea love for learning, personal growth, and compassion.  \nAcknowledgements  \nI believe wholeheartedly that we stand on the shoulders of giants, and my DPhil journey is a reflection of the exceptional mentorship, support, and relationships I’ve been fortunate to have along the way. It is a privilege to express my heartfelt gratitude to some of the individuals and organizations who have made this journey possible.  \nFirst, I am deeply grateful to my supervisor, Dr. David Clifton, for his invaluable mentorship. His guidance has allowed me to explore and contribute to the fascinating field of artificial intelligence in healthcare. He has provided countless opportunities for me to grow, learn, and make meaningful contributions to this domain. His encouragement has continually pushed me to critically engage with the overarching questions and themes of my research and to rise to the rigorous standards of a DPhil. Beyond academics, his lessons in diplomacy and professionalism will inspire me throughout my career. I am committed to carrying forward his example and ensuring that the ladder remains accessible for those who follow.  \nI am thankful for the financial support I received during my studies from the European Union’s Horizon 2020 Research and Innovation funding programme (Marie Skłodowska-Curie Grant No. 955681,“MOIRA”), the Canadian Centennial Scholarship Fund, and additional travel funding from the Department of Engineering Sciences and Balliol College. These contributions were instrumental in enabling my research and academic pursuits.  \nI also want to express my gratitude to the members of the CHI lab and collaborators, who have been my companions throughout this journey. In particular, I am thankful to Dr. Louise Thwaites, Dr. Lei Clifton, and Dr. Andrew Soltan, who often made time to discuss my work and significantly contributed to the development of my skills.  \nBeginning my DPhil at the height of the COVID-19 pandemic, I am especially grateful to all healthcare workers for their unwavering dedication, courage, and compassion during this unprecedented time. As this thesis focuses on AI developments centered around a COVID-19 case study, I extend my deepest thank","cbCaipEfhYVbGhta","https://ap.wps.com/l/cbCaipEfhYVbGhta","pdf",6639947,1,257,"English","en",105,"# Abstract\n# Acknowledgements\n## Academic supervision and mentorship\n## Funding and travel support\n## CHI lab collaborators and research community\n## COVID-19 period and participating healthcare institutions","[{\"question\":\"What problem does the thesis focus on?\",\"answer\":\"The thesis focuses on fairness and bias in clinical machine learning models, particularly how biased algorithms can perpetuate healthcare disparities.\"},{\"question\":\"How does the research evaluate fairness-aware approaches?\",\"answer\":\"It uses case studies and empirical analyses, examining how biases manifest and influence model performance across diverse patient populations.\"},{\"question\":\"What kind of data is used to propose and assess mitigation techniques?\",\"answer\":\"The thesis draws on datasets from multiple healthcare institutions and evaluates fairness-aware techniques aimed at advancing equitable patient outcomes.\"}]","Algorithmic Fairness and Bias Mitigation in Clinical Machine Learning for Equitable Patient Outcomes - 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