[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122535-en":3,"doc-seo-122535-105":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},122535,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Optimizing Hard-to-Place Kidney Allocation - A Machine Learning Approach to Center Ranking","Kidney transplantation is the preferred treatment for end-stage renal disease, yet limited donor supply and inefficiencies in allocation systems cause major bottlenecks, extending wait times and contributing to high mortality. Even for life-saving organs, timely and effective interventions to prevent non-utilization are insufficient. The paper presents a data-driven machine learning ranking policy that predicts each transplant center’s acceptance likelihood for hard-to-place kidneys and prioritizes offers accordingly. Experiments show substantially fewer centers considered before placement and improved utilization potential, with interpretability analyses to identify decision drivers.","arXiv :2410 .09116v1 [ cs .GT] 10 Oct 2024  \nOptimizing Hard-to-Place Kidney Allocation: A Machine Learning Approach to Center Ranking  \nSean Berry  \nDepartment of Mechanical, Industrial and Mechatronics Engineering, Toronto Metropolitan University, Toronto, ON M5S 3G8, [sean.berry@torontomu.ca](sean.berry@torontomu.ca)  \nBerk G¨org¨ul¨u  \nMcMaster University, DeGroote School of Business, Hamilton, ON L8S 4M4, [gorgulub@mcmaster.ca](gorgulub@mcmaster.ca)  \nSait Tun¸c  \nVirginia Tech, Grado Department of Industrial and Systems Engineering, Blacksburg, VA 24061, [sait.tunc@vt.edu](sait.tunc@vt.edu)  \nMucahit Cevik  \nDepartment of Mechanical, Industrial and Mechatronics Engineering, Toronto Metropolitan University, Toronto, ON M5S 3G8, [mcevik@torontomu.ca](mcevik@torontomu.ca)[ ](mcevik@torontomu.ca)[Matthew J. Ellis](Matthew J. Ellis)  \nDuke University School of Medicine, Department of Medicine, Durham, NC 27710  \nAbstract  \nKidney transplantation is the preferred treatment for end-stage renal disease, yet the scarcity of donors and inefficiencies in allocation systems create major bottlenecks, resulting in prolonged wait times and alarming mortality rates. Despite their severe scarcity, timely and effective interventions to prevent non-utilization of life-saving organs remain inadequate. Expedited out-of-sequence placement of hard-to-place kidneys to centers with the highest likelihood of utilizing them has been recommended in the literature as an effective strategy to improve placement success. Nevertheless, current attempts towards this practice is nonstandardized and heavily rely on the subjective judgment of the decisionmakers. This paper proposes a novel data-driven, machine learning-based ranking system for allocating hard-to-place kidneys to centers with a higher likelihood of accepting and successfully transplanting them. Using the national deceased donor kidney offer and transplant datasets, we construct a unique dataset with donor-, center-, and patient-specific features. We propose a data-driven out-of-sequence placement policy that utilizes machine learning models to predict the acceptance probability of a given kidney by a set of transplant centers, ranking them accordingly based on their likelihood of acceptance. Our experiments demonstrate that the proposed policy can reduce the average number of centers considered before placement by fourfold for all kidneys and tenfold for hard-toplace kidneys. This significant reduction indicates that our method can improve the utilization of hard-to-place kidneys and accelerate their acceptance, ultimately reducing patient mortality and the risk of graft failure. Further, we utilize machine learning interpretability tools to provide insights into factors influencing the kidney allocation decisions.  \nKeywords: Kidney allocation, hard-to-place organs, expedited organ placement, machine learning, model interpretability  \n1 Introduction  \nKidney disease is the ninth leading cause of death in the US and represents a significant global health challenge, with millions of people affected by endstage renal disease (ESRD) [19] . The preferred and most effective treatment for ESRD is a kidney transplant. However, the scarcity of donor kidneys and inefficiencies in allocation systems create significant bottlenecks, leading to lengthy wait times and increased waitlist mortality rates [12, 21] . This scarcity is further exacerbated by the high non-utilization rates of viable kidneys, which often result from a combination of logistical challenges, stringent organselection criteria of transplant centers, and the time-sensitive nature of organ transplantation [35] . The non-utilization rate for hard-to-place kidneys, such as those from older donors, donors with medical comorbidities, or kidneys with extended ischemic times is particularly concerning. Non-utilization rates increase steadily with higher Kidney Donor Profile Index (KDPI) scores, a  \nmetric that combines several factors to esti","cbCaipwKD30RFm4K","https://ap.wps.com/l/cbCaipwKD30RFm4K","pdf",2200952,1,39,"English","en",105,"# Introduction\n## Clinical problem and allocation bottlenecks\n## Hard-to-place organs and non-utilization\n## Existing interventions and limitations\n## Proposed expedited out-of-sequence ranking approach","[{\"question\":\"What problem does the paper address in kidney transplantation allocation?\",\"answer\":\"It addresses inefficiencies and donor scarcity that lead to long wait times and high mortality, compounded by high non-utilization of hard-to-place kidneys.\"},{\"question\":\"How does the proposed method allocate hard-to-place kidneys to transplant centers?\",\"answer\":\"It builds a data-driven, machine learning–based ranking system that predicts the acceptance probability of a given kidney by candidate centers and ranks centers by that likelihood.\"},{\"question\":\"What results does the paper report from its experiments?\",\"answer\":\"The policy reduces the average number of centers considered before placement by fourfold overall and tenfold for hard-to-place kidneys, indicating improved acceptance and utilization.\"}]","Optimizing Hard-to-Place Kidney Allocation - A Machine Learning Approach to Center Ranking | PDF",1785811140,98,{"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},"optimizing-hard-to-place-kidney-allocation-a-machine-learning-approach-to-center-ranking","",{"@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/optimizing-hard-to-place-kidney-allocation-a-machine-learning-approach-to-center-ranking/122535/",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","2026-08-04",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 problem does the paper address in kidney transplantation allocation?","Question",{"text":75,"@type":76},"It addresses inefficiencies and donor scarcity that lead to long wait times and high mortality, compounded by high non-utilization of hard-to-place kidneys.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method allocate hard-to-place kidneys to transplant centers?",{"text":80,"@type":76},"It builds a data-driven, machine learning–based ranking system that predicts the acceptance probability of a given kidney by candidate centers and ranks centers by that likelihood.",{"name":82,"@type":73,"acceptedAnswer":83},"What results does the paper report from its experiments?",{"text":84,"@type":76},"The policy reduces the average number of centers considered before placement by fourfold overall and tenfold for hard-to-place kidneys, indicating improved acceptance and utilization.","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"]