[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121315-en":3,"doc-seo-121315-105":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":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},121315,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","MACHINE LEARNING THAT MAKES SENSE IN CLINICAL SETTINGS - A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy","This dissertation presents a health services research study focused on how machine learning methods can be meaningfully applied within clinical environments. It introduces the context, motivations, problem statement, and research questions, then develops the dissertation’s structure through an experimental and theoretical foundation. The work reviews major machine learning paradigms, including supervised, unsupervised, semi-supervised learning, and neural approaches, and connects them to medical use cases such as electronic health records, clinical decision support, uncertainty, probabilistic reasoning, and ICU risk prediction and severity scoring. It also addresses adoption challenges for implementation.","MACHINE LEARNING THAT MAKES SENSE IN CLINICAL SETTINGS  \nby  \nEman Elashkar  \nA Dissertation  \nSubmitted to the  \nGraduate Faculty  \nof  \nGeorge Mason University  \nin Partial Fulfillment of  \nThe Requirements for the Degree  \nof  \nDoctor of Philosophy  \nHealth Services Research  \nCommittee:  \n  Dr. Janusz Wojtusiak, PhD  \nChair  \nGMU, Health Administration and Policy  \nDr. Hua Min, PhD  \n__________________________________ Committee Member  \nGMU, Health Administration and Policy  \n  Dr. Sanja Avramovic, PhD  \nCommittee Member  \nGMU, Health Administration and Policy  \n.  \n  Dr. Gilbert Gimm, PhD  \nProgram Director  \n  Dr. PJ Maddox, Department Chair  \nDate:   Summer Semester 2024  \nGeorge Mason University  \nFairfax, VA  \nMACHINE LEARNING THAT MAKES SENSE IN CLINICAL SETTINGS  \nA Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy  \nBy  \nEman Elashkar  \nBachelor of Medicine and Bachelor of Surgery  \nKasr Al Aini School of Medicine, Cairo University  \nDirector: Janusz Wojtusiak, Professor  \nCollege of Public Health, George Mason University  \nSummer Semester 2024  \nGeorge Mason University  \nFairfax, VA  \nCopyright 2024 Eman Elashkar All Rights Reserved  \nDEDICATION  \nTo my family, whose unwavering love and support have been my bedrock; to my parents, whose encouragement, and belief in me have been my greatest strength; to my friends, whose companionship have provided me with joy and motivation throughout this process, and to my esteemed colleagues, whose collaboration and insights have been invaluable in pursuing this work. With heartfelt gratitude and deep appreciation, this dissertation is dedicated to all of you.  \nACKNOWLEDGMENTS  \nI wish to thank my advisor, Dr. Janusz Wojtusiak, for his guidance and support throughout this process and my whole health informatics career. I also want to thank the members of my committee, Dr. Hua Min and Dr. Sanja Avramovic for their help and support. Finally, my work would not have been possible without the support of my colleagues.  \nTABLE OF CONTENTS  \nLIST OF TABLES ............................................................................................................. xi  \nLIST OF FIGURES .......................................................................................................... xii  \nLIST OF EQUATIONS ................................................................................................... xiv  \nLIST OF ABBREVIATIONS AND SYMBOLS ............................................................. xv  \nABSTRACT..................................................................................................................... xvi  \nINTRODUCTION .............................................................................................................. 1  \nContext ................................................................................................................ 1  \nMotivations.......................................................................................................... 3  \nProblem statement ............................................................................................... 5  \nResearch Questions and Dissertation Aims ........................................................ 8  \nEXPERIMENT 1 ........................................................................................... 13  \nContributions and Deliverables ......................................................................... 15  \nOverview of Dissertation and Roadmap ........................................................... 16  \nTHEORETICAL BACKGROUND.................................................................................. 18  \nOverview of Machine Learning ........................................................................ 18  \nTypes of Machine Learning .............................................................................. 19  \nSUPERVISED LEARNING..............................................","cbCaijMETnxUmap8","https://ap.wps.com/l/cbCaijMETnxUmap8","pdf",1876112,1,200,"English","en",105,"# Introduction\n## Context\n## Motivations\n## Problem statement\n## Research Questions and Dissertation Aims\n# Experiment 1\n## Contributions and Deliverables\n## Overview of Dissertation and Roadmap\n# Theoretical Background\n## Overview of Machine Learning\n## Types of Machine Learning\n### Supervised Learning\n### Unsupervised Learning\n### Dimensions Reduction\n### Clustering\n### Association\n### Semi-Supervised Learning\n### Artificial Neural Networks\n### Reinforcement Learning\n### Temporal Difference Learning\n## Machine Learning in Medicine\n## Electronic Health Records\n## Clinical Decision Support Systems (CDSS)\n## Medical Decision Making\n## Uncertainty\n## Probabilistic Medical Reasoning\n## Machine Learning in ICU\n## Risk Prediction and Severity Scores in ICU\n## Adoption and Challenges of ML Implementation in ICU\n## Review of Characteristics of ML Models in Clinical Field\n## Guidelines/Framework on Building Models in Clinical Settings","[{\"question\":\"What is the dissertation’s main focus in clinical settings?\",\"answer\":\"It examines how machine learning can be applied in meaningful ways within clinical environments, with emphasis on medical decision support and patient-risk related tasks.\"},{\"question\":\"Which machine learning approaches are covered in the theoretical background?\",\"answer\":\"The document reviews supervised, unsupervised, semi-supervised learning, as well as topics such as dimensionality reduction, clustering, association, artificial neural networks, reinforcement learning, and temporal difference learning.\"},{\"question\":\"How does the dissertation connect machine learning to healthcare workflows?\",\"answer\":\"It discusses applications such as electronic health records, clinical decision support systems, uncertainty handling, probabilistic medical reasoning, and specific ICU risk prediction and severity scoring tasks, along with adoption challenges.\"}]","MACHINE LEARNING THAT MAKES SENSE IN CLINICAL SETTINGS - A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy | PDF",1785735026,504,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-that-makes-sense-in-clinical-settings-a-dissertation-submitted-in-partial-fulfillment-of-the-requirements-for-the-degree-of-doctor-of-philosophy","",{"@graph":36,"@context":85},[37,54,68],{"@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/machine-learning-that-makes-sense-in-clinical-settings-a-dissertation-submitted-in-partial-fulfillment-of-the-requirements-for-the-degree-of-doctor-of-philosophy/121315/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the dissertation’s main focus in clinical settings?","Question",{"text":75,"@type":76},"It examines how machine learning can be applied in meaningful ways within clinical environments, with emphasis on medical decision support and patient-risk related tasks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning approaches are covered in the theoretical background?",{"text":80,"@type":76},"The document reviews supervised, unsupervised, semi-supervised learning, as well as topics such as dimensionality reduction, clustering, association, artificial neural networks, reinforcement learning, and temporal difference learning.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the dissertation connect machine learning to healthcare workflows?",{"text":84,"@type":76},"It discusses applications such as electronic health records, clinical decision support systems, uncertainty handling, probabilistic medical reasoning, and specific ICU risk prediction and severity scoring tasks, along with adoption challenges.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]