[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118964-en":3,"doc-seo-118964-105":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},118964,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Towards Robust and Generalizable Machine Learning for Real-World Healthcare Data with Heterogeneity - Dissertation","Machine learning is positioned as a central approach for improving health and well-being, supported by the growing digitization of hospital records and the resulting abundance of training data. Real-world deployment of mobile and clinical health solutions faces major gaps between advanced models and clinical requirements, including fairness, personalized treatment via precise phenotyping, and longitudinal data shift. The work addresses domain-, class-, and personal-level heterogeneity by developing robust, generalizable and multimodal learning models that continually update risk representations and improve both predictive accuracy and interpretability.","TOWARDS ROBUST AND GENERALIZABLE MACHINE LEARNING FOR REAL-WORLD  \nHEALTHCARE DATA WITH HETEROGENEITY  \nA Dissertation  \nby  \nZEPENG HUO  \nSubmitted to the Graduate and Professional School of  \nTexas A&M University  \nin partial fulfillment of the requirements for the degree of  \nDOCTOR OF PHILOSOPHY  \nChair of Committee, Bobak J. Mortazavi Committee Members, Xiaoning Qian  \nZhangyang Wang  \nJames Caverlee  \nHead of Department, Scott Schaefer  \nDecember 2022  \nMajor Subject: Computer Science  \nCopyright 2022 Zepeng Huo  \nABSTRACT  \nThe utility of machine learning for enhancing human well-being and health has risen to the core discussion in both research and real-world application in today’s technological front-line. The fastgrowing artificial intelligence industry has innovated health-related applications. Inversely the realworld challenges in accurate implementation of mobile and clinical health solutions have necessitated advancements in theoretical and algorithmic development. The increasing rate of digitizing medical records in hospitals has enable artificial intelligence with abundant data to train. In return the trained models have shown to lower the uncertainty in clinical decision making. However, as always, with opportunities comes new challenges. We have observed many discrepancies of compatibility between sophisticated machine learning models and the nuanced clinical needs, such as fairness, personalized treatment through precise phenotyping and data shift in longitudinal medical records. In modeling complex and heterogeneous health record-based machine learning and then extrapolating through remote health applications, I have identified the need for advanced, multimodal models that continually learn risk representation from varied, heterogeneous data sources. Broadly, I recognize a few gaps in different levels currently blocking us from enhancing continual machine learning for health: 1) domain-, 2) class-, and 3) personal-level heterogeneity in realworld healthcare data.  \nWith the three proposed aims, I will target at using machine learning in a more robust and generalizable way towards real-world biomedical data and to enhance not only the prediction accuracy but also the interpretation of the results. The proposed work will largely benefit both machine learning domain as well as human-centered computing application. This dissertation will mostly introduce and elaborate how to bridge the gap between algorithm in the lab and the open-world challenges and hopeful will spur more research onto this interdisciplinary problem and bring about real world improvements.  \nDEDICATION  \nTo my mother, my father, my grandfather, and my late grandmother and all the family and friends, locally in College Station or globally around the world, who supported me along the journey.  \nACKNOWLEDGMENTS  \nI would like to thank my committee chair, Dr. Bobak Mortazavi, and my committee members, Dr. Xiaoning Qian, Dr. Zhangyang Wang, and Dr. James Caverlee, for their guidance and support throughout the course of this research. My thanks also go to all the help I have received from Computer Science department at Texas A&M, including my friends, labmates, classmates, colleagues and the department faculty and staff for making my time at Texas A&M University a great experience.  \nCONTRIBUTORS AND FUNDING SOURCES  \nContributors  \nThis work was supported by a thesis (or) dissertation committee consisting of Professor Bobak Mortazaiv [advisor] and Dr. Zhangyang Wang, Dr. James Caverlee of the Department of Computer Science and Engineering and Professor Xiaoning Qian of the Department of Electrical and Computer Engineering.  \nThe data in Section 2.1 for in-house RealActivity motion dataset is collected by my labmate Arash Pakbin. The data in Section 3.2 from TOPCAT is processed by my labmate Nathan Hurley. The data in Section 4.2 for interstitial glucose readings is collected by Center for Translational Research in Aging and Longevity at Texas A&M ","cbCaicpbtkZJRS0s","https://ap.wps.com/l/cbCaicpbtkZJRS0s","pdf",8223370,1,182,"English","en",105,"# Abstract\n# Dedication\n# Acknowledgments\n# Contributors and Funding Sources\n# Nomenclature\n# Table of Contents","[{\"question\":\"What real-world healthcare challenges motivate this dissertation?\",\"answer\":\"The dissertation highlights mismatches between sophisticated machine learning models and nuanced clinical needs, including fairness, personalized treatment through precise phenotyping, and distribution shift across longitudinal medical records.\"},{\"question\":\"How does the dissertation define heterogeneity in real-world healthcare data?\",\"answer\":\"It identifies heterogeneity at three levels that affect learning quality and generalization: domain-level, class-level, and personal-level heterogeneity.\"},{\"question\":\"What is the main goal of the proposed machine learning approach?\",\"answer\":\"The work aims to build robust, generalizable, multimodal models for biomedical data that improve prediction accuracy while enhancing result interpretation through continual learning of risk representations.\"}]","Towards Robust and Generalizable Machine Learning for Real-World Healthcare Data with Heterogeneity - Dissertation | PDF",1785721250,459,{"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},"towards-robust-and-generalizable-machine-learning-for-real-world-healthcare-data-with-heterogeneity-dissertation","",{"@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/towards-robust-and-generalizable-machine-learning-for-real-world-healthcare-data-with-heterogeneity-dissertation/118964/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What real-world healthcare challenges motivate this dissertation?","Question",{"text":75,"@type":76},"The dissertation highlights mismatches between sophisticated machine learning models and nuanced clinical needs, including fairness, personalized treatment through precise phenotyping, and distribution shift across longitudinal medical records.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the dissertation define heterogeneity in real-world healthcare data?",{"text":80,"@type":76},"It identifies heterogeneity at three levels that affect learning quality and generalization: domain-level, class-level, and personal-level heterogeneity.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the main goal of the proposed machine learning approach?",{"text":84,"@type":76},"The work aims to build robust, generalizable, multimodal models for biomedical data that improve prediction accuracy while enhancing result interpretation through continual learning of risk representations.","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"]