[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125919-en":3,"doc-seo-125919-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125919,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",7,"Healthcare","Predicting post–liver transplant outcomes in patients with acute-on-chronic liver failure using Expert-Augmented Machine Learning - Journal article abstract","Liver transplantation treats acute-on-chronic liver failure (ACLF), yet post-transplant mortality remains high and existing prediction models for ACLF are limited. This study builds an Expert-Augmented Machine Learning (EAML) model to predict post–liver transplant outcomes. ACLF patients receiving transplant were identified from the University of California Health Data Warehouse, RuleFit extracted decision rules, and human expert ratings were integrated to form final EAML models. Performance for 1-year and 90-day death and comparisons with baseline methods are reported, and discrepancies highlight biomarker ranking differences.","UCSF  \nUC San Francisco Previously Published Works  \nTitle  \nPredicting post–liver transplant outcomes in patients with acute-on-chronic liver failure using Expert-Augmented Machine Learning  \nPermalink  \n[https://escholarship.org/uc/item/10q1z73c](https://escholarship.org/uc/item/10q1z73c)  \nJournal  \nAmerican Journal of Transplantation, 23(12)  \nISSN  \n1600-6135  \nAuthors  \nGe, Jin  \nDigitale, Jean C Fenton, Cynthia et al.  \nPublication Date  \n2023-12-01  \nDOI  \n10.1016/j.ajt.2023.08.022  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nAuthor Manuscr ipt Author Manuscr ipt Author Manuscr ipt Author Manuscript  \n\n|  | HHS Public Access\u003Cbr>Author manuscript\u003Cbr>Am J Transplant. Author manuscript; available in PMC 2024 September 01. |\n| --- | --- |\n\nPublished in final edited form as:  \nAm J Transplant. 2023 December ; 23(12): 1908–1921. doi:10.1016/j.ajt.2023.08.022 .  \nPredicting post–liver transplant outcomes in patients with acute-on-chronic liver failure using Expert-Augmented Machine Learning  \nJin Ge 1,* , Jean C. Digitale2 , Cynthia Fenton3 , Charles E. McCulloch2 , Jennifer C. Lai 1 , Mark J. Pletcher2 , Efstathios D. Gennatas2  \n1 Division of Gastroenterology and Hepatology, Department of Medicine, University of California– San Francisco, San Francisco, California, USA  \n2 Department of Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, California, USA  \n3 Division of Hospital Medicine, Department of Medicine, University of California–San Francisco, San Francisco, California, USA  \nAbstract  \nLiver transplantation (LT) is a treatment for acute-on-chronic liver failure (ACLF), but high post-LT mortality has been reported. Existing post-LT models in ACLF have been limited. We developed an Expert-Augmented Machine Learning (EAML) model to predict post-LT outcomes.  \nWe identified ACLF patients who underwent LT in the University of California Health Data Warehouse. We applied the RuleFit machine learning (ML) algorithm to extract rules from  \n*Corresponding author. Jin Ge, 513 Parnassus Avenue, S-357 San Francisco, CA 94143, USA., [jin.ge@ucsf.edu](jin.ge@ucsf.edu) (J. Ge) . Author contributions  \nAuthorship was determined using ICMJE recommendations.  \nJ. G.: Study concept and design; data extraction; analysis and interpretation of data; drafting of manuscript; critical revision of the manuscript for important intellectual content; statistical analysis; obtained funding; study supervision  \nJ.C.D.: Analysis and interpretation of data; drafting of manuscript; critical revision of the manuscript for important intellectual content  \nC. F.: Data extraction; critical revision of the manuscript for important intellectual content  \nC.E.M.: Interpretation of data; critical revision of the manuscript for important intellectual content  \nJ.C.L.: Study concept and design; interpretation of data; critical revision of the manuscript for important intellectual content; study supervision  \nM.J.P.: Interpretation of data; critical revision of the manuscript for important intellectual content  \nE.D.G.: Study concept and design; analysis and interpretation of data; critical revision of the manuscript for important intellectual content  \nDeclaration of competing interests  \nThe authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Jin Ge reports financial support was provided by National Center for Advancing Translational Sciences. Jin Ge reports financial support was provided by American Association for the Study of Liver Diseases. Jin Ge reports financial support was provided by American Society of Transplantation. Jin Ge reports financial support was provided by UCSF Liver Center. Charles E. McCulloch reports financial support was provided by National Center for Advancing Translational Sciences. Mark J. Pletcher reports financial support was provided b","cbCaiiLM4dnKW9eG","https://ap.wps.com/l/cbCaiiLM4dnKW9eG","pdf",1360526,3,1,28,"English","en",105,"# Abstract\n## Study approach\n## Model performance and comparisons\n## Expert-vs-ML discrepancy and implications","[{\"question\":\"What clinical problem does this study address?\",\"answer\":\"It targets predicting outcomes after liver transplantation in patients with acute-on-chronic liver failure, where post-transplant mortality is high and existing models are limited.\"},{\"question\":\"How is the Expert-Augmented Machine Learning (EAML) model constructed?\",\"answer\":\"The study uses the RuleFit algorithm to extract rules from ACLF data, then incorporates human expert ratings to generate final EAML models.\"},{\"question\":\"What data source and patient cohort are used?\",\"answer\":\"ACLf patients who underwent liver transplantation were identified from the University of California Health Data Warehouse.\"}]","Predicting post–liver transplant outcomes in patients with acute-on-chronic liver failure using Expert-Augmented Machine Learning - Journal article abstract | PDF",1785902032,71,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"predicting-postliver-transplant-outcomes-in-patients-with-acute-on-chronic-liver-failure-using-expert-augmented-machine-learning-journal-article-abstract","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/healthcare/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/predicting-postliver-transplant-outcomes-in-patients-with-acute-on-chronic-liver-failure-using-expert-augmented-machine-learning-journal-article-abstract/125919/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What clinical problem does this study address?","Question",{"text":76,"@type":77},"It targets predicting outcomes after liver transplantation in patients with acute-on-chronic liver failure, where post-transplant mortality is high and existing models are limited.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the Expert-Augmented Machine Learning (EAML) model constructed?",{"text":81,"@type":77},"The study uses the RuleFit algorithm to extract rules from ACLF data, then incorporates human expert ratings to generate final EAML models.",{"name":83,"@type":74,"acceptedAnswer":84},"What data source and patient cohort are used?",{"text":85,"@type":77},"ACLf patients who underwent liver transplantation were identified from the University of California Health Data Warehouse.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,119,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":117,"slug":118},40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":47,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]