[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117031-en":3,"doc-seo-117031-105":29,"detail-sidebar-cat-0-en-105":90},{"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":13,"seo_description":14,"update_tm":27,"read_time":28},117031,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Machine Learning Methods for Personalized Healthcare","The dissertation addresses escalating healthcare costs and the growing burden of chronic diseases by leveraging machine learning to enable more personalized and efficient care. It examines key challenges in the healthcare setting: privacy limits cross-institution data sharing and feature collection, and models must be explainable for clinicians. The work proposes solutions for both hospital and remote monitoring, including adaptive data acquisition, unsupervised intracranial hemorrhage segmentation, and remote health monitoring algorithms, aiming to improve outcomes while reducing costs.","UCLA  \nUCLA Electronic Theses and Dissertations  \nTitle  \nMachine Learning Methods for Personalized Healthcare  \nPermalink  \n[https://escholarship.org/uc/item/3f49v98x](https://escholarship.org/uc/item/3f49v98x)  \nAuthor  \nKarkkainen, Kimmo  \nPublication Date  \n2023  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA Los Angeles  \nMachine Learning Methods for Personalized Healthcare  \nA dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Computer Science  \nby  \nKimmo Karkkainen  \n2023  \n© Copyright by Kimmo Karkkainen  \n2023  \nABSTRACT OF THE DISSERTATION  \nMachine Learning Methods for Personalized Healthcare  \nby  \nKimmo Karkkainen  \nDoctor of Philosophy in Computer Science  \nUniversity of California, Los Angeles, 2023  \nProfessor Majid Sarrafzadeh, Chair  \nThe escalating cost of healthcare and the growing prevalence of chronic diseases have created an urgent need for new solutions. Machine learning has the potential to revolutionize healthcare by providing more personalized and efficient care. However, there are unique challenges associated with applying machine learning in healthcare. Privacy concerns prevent data sharing across institutions, which limits available training data, and collecting individual features for patients may be invasive or expensive, as they may involve lab tests or medical imaging. In addition, machine learning models must be explainable so that medical professionals can understand how they arrive at a certain diagnosis. Despite these challenges, machine learning presents new opportunities in healthcare, both in hospitals and in remote health monitoring. In hospitals, machine learning can improve efficiency by assisting medical professionals with patient diagnoses, while in remote health monitoring, the vast quantities of data from personal and wearable devices open new opportunities for preventative care. However, processing and extracting meaningful insights from healthcare data require novel techniques. This dissertation investigates solutions for personalized healthcare in both hospital and remote healthcare settings, including adaptive data acquisition, unsupervised medical image segmentation, and remote health monitoring algorithms and applications. Overall, these solutions have the potential to improve patient outcomes and reduce healthcare costs.  \nThe dissertation of Kimmo Karkkainen is approved.  \nJungseock Joo  \nCho-Jui Hsieh Guy Van den Broeck Majid Sarrafzadeh, Committee Chair  \nUniversity of California, Los Angeles 2023  \nTo Ela    \niv  \nTABLE OF CONTENTS  \n1 Introduction ...................................... 1  \n2 Cost-Sensitive Feature-Value Acquisition Using Feature Relevance ... 4  \n2.1 Introduction .................................... 4  \n2.2 Related Work ................................... 6  \n2.3 Relevance Propagation .............................. 8  \n2.4 Our Approach ................................... 10  \n2.4.1 Problem Definition ............................ 10  \n2.4.2 Direct Propagation ............................ 12  \n2.4.3 Multiple Propagations .......................... 15  \n2.4.4 Implementation details .......................... 15  \n2.5 Experiments and Results ............................. 17  \n2.5.1 Diabetes Prediction ............................ 17  \n2.5.2 Heart Disease Prediction ......................... 19  \n2.5.3 Learning to Rank Competition ..................... 21  \n2.6 Discussion ..................................... 22  \n2.7 Conclusion ..................................... 24  \n3 Unsupervised Intracranial Hemorrhage Segmentation With Gaussian Mixture Models ........................................ 25  \n3.1 Introduction .................................... 25  \n3.2 Related Works ................................... 28  \n3.3 Methodology ................................... 29  \n3.3.1 Data ..","cbCaisE7GCkofhIj","https://ap.wps.com/l/cbCaisE7GCkofhIj","pdf",3287529,1,131,"English","en",105,"# Introduction\n# Cost-Sensitive Feature-Value Acquisition Using Feature Relevance\n## Introduction\n## Related Work\n## Relevance Propagation\n## Our Approach\n## Experiments and Results\n## Discussion\n## Conclusion\n# Unsupervised Intracranial Hemorrhage Segmentation With Gaussian Mixture Models\n## Introduction\n## Related Works\n## Methodology\n## Experiments And Results\n## Discussion\n## Conclusion\n# Sleep and Activity Prediction for Type 2 Diabetes Management Using Continuous Glucose Monitoring\n## Introduction\n## Methods\n## Results\n## Discussion\n## Conclusion\n# Identifying Substance Use and High-Risk Sexual Behavior Using Mobile Phone Data","[{\"question\":\"What problems does the dissertation target in personalized healthcare?\",\"answer\":\"It targets rising healthcare costs and the increasing prevalence of chronic diseases. It focuses on turning healthcare data into effective personalized care while addressing practical challenges of model development and deployment.\"},{\"question\":\"Why is privacy a central challenge for machine learning in healthcare?\",\"answer\":\"Privacy concerns restrict data sharing across institutions, reducing available training data. Collecting patient-specific features can also be invasive or expensive, involving lab tests or medical imaging.\"},{\"question\":\"What kinds of solutions does the dissertation propose?\",\"answer\":\"It proposes methods for both hospital and remote healthcare settings, including adaptive data acquisition, unsupervised medical image segmentation for intracranial hemorrhage, and algorithms for remote health monitoring and related predictive tasks.\"}]",1785673201,330,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"machine-learning-methods-for-personalized-healthcare","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/machine-learning-methods-for-personalized-healthcare/117031/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problems does the dissertation target in personalized healthcare?","Question",{"text":74,"@type":75},"It targets rising healthcare costs and the increasing prevalence of chronic diseases. It focuses on turning healthcare data into effective personalized care while addressing practical challenges of model development and deployment.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Why is privacy a central challenge for machine learning in healthcare?",{"text":79,"@type":75},"Privacy concerns restrict data sharing across institutions, reducing available training data. Collecting patient-specific features can also be invasive or expensive, involving lab tests or medical imaging.",{"name":81,"@type":72,"acceptedAnswer":82},"What kinds of solutions does the dissertation propose?",{"text":83,"@type":75},"It proposes methods for both hospital and remote healthcare settings, including adaptive data acquisition, unsupervised medical image segmentation for intracranial hemorrhage, and algorithms for remote health monitoring and related predictive tasks.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]