[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120997-en":3,"doc-seo-120997-105":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},120997,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Constrained Machine Learning Methods for Biomedical Data Analysis","Machine learning methods deliver strong performance across computational tasks, and their role in health data science is increasingly relevant for clinicians seeking better disease understanding and improved clinical pathways. This thesis addresses the gap in tailoring general ML methodology to health settings by explicitly incorporating constraints into model design. The proposed constrained approaches preserve ML performance while improving plausibility and interpretability relative to traditional statistical outputs. Results are demonstrated across pseudotemporal modeling, time-to-event (survival) modeling, and longitudinal survival analysis.","Constrained Machine Learning Methods for Biomedical Data  \nAnalysis  \nBy  \nDominic Danks  \nA thesis submitted to the University of Birmingham for the degree of  \nDOCTOR OF PHILOSOPHY  \nInstitute of Cancer & Genomic Sciences College of Medical and Dental Sciences  \nUniversity of Birmingham January 2023  \nUniversity of Birmingham Research Archive  \ne-theses repository  \nThis unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation.  \nAny use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder.  \nABSTRACT  \nIn recent years, machine learning (ML) methods have shown great promise in a variety of application settings, with ML-based systems now representing the state of the art in a wide variety of intelligence-based computational tasks ranging from image classification to natural language processing. Their impact in health data science is also becoming more tangible, with ML-based systems beginning to be adopted as genuinely attractive prospects by clinicians and health professionals as tools to improve disease understanding and clinical pathways. The importance of such models is also likely to continue to grow as health settings become increasingly data-aware and data-driven.  \nHowever, the field of ML-based health data science is far from fully developed, with many clinically relevant settings not yet fully explored and with limited efforts to consider how general ML methodology can be tailored to the health data science setting. In this thesis, we show that by considering the constraints relevant to health-based ML problems and explicitly involving these in our models, it is possible to derive novel approaches which tend to retain the performance synonymous with machine learning whilst offering the plausibility and interpretability of model outputs typically associated with more traditional statistical approaches.  \nThis thesis demonstrates this approach in three health-relevant settings. We begin by considering the problem of pseudotemporal modelling, where we highlight that it is often the case that some prior knowledge is present about the pseudotemporal trajectory which can  \nbe exploited to aid learning and reduce human burden. Next, we consider the problem of time-to-event modelling (survival analysis) from a novel machine learning perspective and demonstrate that by suitably constraining a neural network it is possible to model general survival functions and in turn obtain strong model performance with little model tuning or computational difficulty. Finally, we consider the problem of survival analysis in the presence of longitudinal data and present a series of approaches which derive from our constrained general survival model and offer various benefits over established methods.  \nACKNOWLEDGEMENTS  \nI have numerous people to thank for making my PhD experience so enjoyable and enriching. I would first like to thank Chris Yau for his excellent supervision throughout. As well as performing the usual tasks expected of a PhD supervisor — insisting on more figures in papers and trying to decode feedback from a “Reviewer 2” —Chris went above and beyond to facilitate opportunities and collaboration outside of what would have otherwise been possible, for which I am truly grateful. He also provided a fountain of great advice at various points throughout the PhD and made for an entertaining Slack buddy during England football matches!  \nI would also like to thank Alastair Denniston and Pearse Keane for welcoming me into their community and for reminding me of the end goal for much of the work in this space, namely to improve the treat","cbCaid4QwmmyV6Iy","https://ap.wps.com/l/cbCaid4QwmmyV6Iy","pdf",9111294,1,147,"English","en",105,"# Abstract\n# Introduction and Motivation\n## Constrained modeling for health data science\n# Methodology and Contributions\n## Pseudotemporal modelling\n## Time-to-event modeling (survival analysis)\n## Longitudinal survival analysis with constrained models","[{\"question\":\"What is the main idea of this thesis's constrained machine learning approach?\",\"answer\":\"The thesis incorporates constraints relevant to health-based machine learning problems directly into the model, aiming to retain machine learning performance while improving the plausibility and interpretability of outputs.\"},{\"question\":\"How does the thesis treat pseudotemporal modelling?\",\"answer\":\"It highlights that prior knowledge about a pseudotemporal trajectory can be exploited to support learning and reduce human burden.\"},{\"question\":\"What survival analysis settings are covered?\",\"answer\":\"The thesis first presents time-to-event modelling from a machine learning perspective using constrained neural networks, then extends constrained survival modelling to cases with longitudinal data via multiple proposed approaches.\"}]","Constrained Machine Learning Methods for Biomedical Data Analysis | 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