[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119161-en":3,"doc-seo-119161-105":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},119161,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","DEVELOPMENT OF A MACHINE LEARNING MODEL FOR LIVER TRANSPLANTATION - Dissertation Abstract","There are far fewer donor livers available for transplant than patients who need them, driving reliance on marginal livers for carefully selected cases. Decisions about matching marginal donor livers to recipients have often relied on clinical gestalt or traditional regression-based studies, which may not capture complex interactions among donor, recipient, and transplant factors. This dissertation develops machine learning approaches to generate personalized predictions, support optimal decision making for marginal organ transplantation, and improve patient-physician communication using national registry data.","DEVELOPMENT OF A MACHINE LEARNING MODEL FOR LIVER  \nTRANSPLANTATION  \nby  \nVictoria Anne Bendersky  \nA dissertation submitted to Johns Hopkins University in conformity with the requirements  \nfor the degree of Doctor of Philosophy.  \nBaltimore, Maryland  \nJuly 2023  \nABSTRACT  \nThere are far fewer donor livers available for transplant than patients who need them. This has led to the use of marginal livers – livers that are riskier than typical donor livers yet might still provide a benefit to carefully selected patients. However, the decision to use a particular marginal liver for a particular patient is based largely on clinical gestalt or traditional clinical studies often using regression analysis , which likely do not fully account for the complex relationships between donor, recipient, and transplant characteristics. With the continued advancement of technology and supervised learning computer algorithms, machine learning (ML) methods have emerged as a valuable means of leveraging rich database information to generate personalized predictions (Chapter 1) . This dissertation focuses on the utilization of ML algorithms to optimally inform decision making around marginal organ transplantation and enhance patientphysician communication.  \nWe began by leveraging Scientific Registry of Transplant Recipients (SRTR) national data and employing several machine learning techniques to determine which patient characteristics held the most importance in predicting their survival on the waitlist. We then took this output and constructed a waitlist survival model (Chapter 2) . Using the same database and machine learning methods, we developed a model that would predict post-transplant survival for a specific patient-liver pairing (Chapter 3) .  \nWe then interviewed liver transplant candidates, recipients, and healthcare providers to ascertain stakeholder priorities in designing a decision aid that displays the two aforementioned survival predictions (Chapter 4) . Lastly, as all these efforts would be in vain if we could not improve upon long-term survival of the organ, we sought to gain  \ninsight into the post-transplantation patient experience and barriers to immunosuppression medication adherence (Chapter 5) . We conclude with a summary of significant findings and plans for future research (Chapter 6) .  \nThesis Committee  \nResearch Mentor  \nDorry L. Segev, MD PhD  \nAcademic Advisor  \nO. Joseph Bienvenu, MD PhD  \nThesis Readers  \nBrian S. Caffo, PhD Elizabeth Ann King, MD PhD  \nAlternates  \nKaren Bandeen-Roche, PhD Khalil Ghanem, MD PhD  \nACKNOWLEDGEMENTS  \nThis work was supported by a Ruth L. Kirschstein National Research Service Award for Individual Postdoctoral Fellowship from the National Institute of Diabetes and Digestive and Kidney Diseases (F32DK124962) . The contents of this dissertation are solely the responsibility of the author and do not necessarily represent the official views of the Johns Hopkins Medical Institutions or the National Institutes of Health.  \nThis work would not have been possible without the support of the following:  \nSegev—Thank you so much for meeting with me on such short notice as I spontaneously flew to Baltimore to initiate this project and for enthusiastically welcoming general surgery residents into the lab.  \nERGOT/TRC/C-STAR—No matter the name or institution, I would like to thank everyone I met along the way , especially: Drs. Betsy King, Jackie Garonzik-Wang, Macey Levan, Sommer Gentry, Allan Massie, William Werbel, Dan Warren, Aly Strauss, Karen Vanterpool, Kyle Jackson, Amber Kernodle, Brian Boyarsky , Mickey Eagleson, Jessica Ruck, Jennifer Alejo , Hannah Sung, and other lab members, Amrita Saha, Carolyn Sidoti, Alexander Ferzola, Max Downey, and Ellie Kim among so many others.  \nSchool of Public Health and Graduate Training Program in Clinical Investigation Staff/Faculty – Thank you for establishing and maintaining such a unique program for clinicians to pursue additional doctoral trainin","cbCaif86ov9C4JuK","https://ap.wps.com/l/cbCaif86ov9C4JuK","pdf",1469981,1,124,"English","en",105,"# Abstract\n# Acknowledgements\n# Chapter 1 – Introduction\n# Chapter 2 – Development of a Machine Learning Model for Liver Waitlist Survival\n# Chapter 3 – Development of a Machine Learning Model for Marginal Livers","[{\"question\":\"Why does liver transplantation require predictive modeling in this dissertation?\",\"answer\":\"Donor livers are limited, so clinicians use marginal livers that carry higher risk. Matching these livers to patients requires better prediction than traditional approaches provide.\"},{\"question\":\"What data and modeling approach are used in the study?\",\"answer\":\"The dissertation uses Scientific Registry of Transplant Recipients (SRTR) national data and applies multiple machine learning techniques to identify predictors and build survival models.\"},{\"question\":\"How do the models support clinical decision making and communication?\",\"answer\":\"The work generates waitlist and post-transplant survival predictions for specific patient–liver pairings, then gathers stakeholder priorities to design a decision aid that improves patient-physician communication.\"}]","DEVELOPMENT OF A MACHINE LEARNING MODEL FOR LIVER TRANSPLANTATION - Dissertation Abstract | PDF",1785722835,312,{"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},"development-of-a-machine-learning-model-for-liver-transplantation-dissertation-abstract","",{"@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/development-of-a-machine-learning-model-for-liver-transplantation-dissertation-abstract/119161/",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},"Why does liver transplantation require predictive modeling in this dissertation?","Question",{"text":75,"@type":76},"Donor livers are limited, so clinicians use marginal livers that carry higher risk. Matching these livers to patients requires better prediction than traditional approaches provide.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and modeling approach are used in the study?",{"text":80,"@type":76},"The dissertation uses Scientific Registry of Transplant Recipients (SRTR) national data and applies multiple machine learning techniques to identify predictors and build survival models.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the models support clinical decision making and communication?",{"text":84,"@type":76},"The work generates waitlist and post-transplant survival predictions for specific patient–liver pairings, then gathers stakeholder priorities to design a decision aid that improves patient-physician communication.","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"]