[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127418-en":3,"doc-seo-127418-105":30,"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":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},127418,962085564807,"Aurelia","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Forecasting Consent in Organ Donation - Early Assessment of Machine-Learning Techniques","Accurately forecasting whether next-of-kin consent for organ donation will be granted is critical for optimizing timing and resource use in donor management. The study develops and evaluates machine-learning models that estimate consent likelihood using donor and contextual factors. Real-world data from a regional transplant center under an opt-in system supports preprocessing, feature selection, and training across multiple algorithms. Standard classification metrics assess performance and key predictors are identified. Results reach above 80% accuracy, emphasizing relatives’ decision information and differences by donor nationality.","Forecasting Consent in Organ Donation: Early Assessment of Machine-Learning Techniques  \nArianna Freda  \nORCID: 0009-0006-8856-9630 Roma Tre University Rome, Italy  \nEmail: [arianna.freda@uniroma3.it](arianna.freda@uniroma3.it)  \nDavide Maestosi  \nRoma Tre University Rome, Italy  \nEmail: [davide.maestosi@stud.uniroma3.it](davide.maestosi@stud.uniroma3.it)  \nMaurizio Naldi  \nORCID: 0000-0002-0903-398XLUMSA University Rome, Italy [Email: m.naldi@lumsa.it](Email: m.naldi@lumsa.it)  \nGaia Nicosia  \nORCID: 0000-0003-4043-2812 Roma Tre University Rome, Italy  \nEmail: [gaia.nicosia@uniroma3.it](gaia.nicosia@uniroma3.it)  \nAndrea Pacifici  \nORCID: 0000-0001-6144-0024 “Tor Vergata” University Rome, Italy  \nEmail: andrea.pacifi[ci@uniroma2.eu](ci@uniroma2.eu)  \nAbstract—Accurately predicting whether consent for organ donation will be granted is essential for optimizing timing and resource use in donor management. This study develops and evaluates machine learning models to estimate the likelihood of obtaining consent based on donor and contextual factors. The goal is to support early clinical decision-making by identifying cases where consent is more or less likely. Using real-world data from a regional transplant center operating under an opt-in system, we conduct data preprocessing, feature selection, and model training with various algorithms. Model performance is assessed using standard classification metrics, and key predictors of consent outcomes are identified. Results show accuracy levels exceeding 80%, highlighting the importance of including information about the relatives responsible for the decision. We also find that prediction accuracy varies with donor nationality, being higher for non-Italian donors. These findings demonstrate the value of predictive analyticsin improving organ procurement efficiency and reducing unnecessary costs.  \nIndex Terms—Health care, Modeling and prediction, Machine learning, Decision support  \nI. INTRODUCTION  \nORGAN transplantation remains the most effective  \nand economically viable treatment for patients with end-stage organ failure. However, despite ongoing efforts to raise awareness and increase donor registrations, a persistent and critical gap remains between the demand for and supply of transplantable organs [1], [2] . Among the key barriers contributing to this shortage is the high rate of opposition to organ donation, particularly when consent must be obtained post-mortem from next-of-kin.  \nThis work has been partially funded by the European Union–Next Generation EU within the framework of PRIN 2022 Project“MEDICINE+AI, Law and Ethics for an Augmented and HumanCentered Medicine”(2022YB89EH)– CUP E53D23007020006  \nThe acquisition of consent is a pivotal step in the organ donation process. In systems where explicit authorization is required—such as the opt-in framework in place in many regions—the inability to secure timely consent can lead to lost donation opportunities, unnecessary delays, and avoidable costs. Even in jurisdictions with opt-out legislation, in practice, healthcare professionals often still seek family confirmation, making the consent process a practical and ethical bottleneck.  \nIn this paper, we propose a data-driven approach to support and optimize the consent acquisition phase through predictive modeling. Specifically, we develop and evaluate machine learning models aimed at forecasting the likelihood of obtaining consent for organ donation. By leveraging available demographic, clinical, and contextual data, our models seek to provide early estimates of consent probability, offering valuable decision support for clinicians involved in donor management.  \nAs it is better discussed in Section III-B, this predictive capability has the potential to significantly enhance operational efficiency. For example, if a high probability of consent is anticipated, preparatory clinical activities can be initiated earlier, minimizing the time to transplantation and reducing organ deteriorati","cbCaiiS5wUyP4UhV","https://ap.wps.com/l/cbCaiiS5wUyP4UhV","pdf",169586,1,10,"English","en",105,"# Introduction\n## Consent acquisition in organ donation processes\n## Proposed data-driven predictive approach\n## Contributions and paper organization","[{\"question\":\"What problem does this study address in organ donation?\",\"answer\":\"The study addresses the challenge of predicting whether consent for organ donation will be granted, which is crucial when consent must be obtained post-mortem from next-of-kin.\"},{\"question\":\"How are the machine-learning models used to support early decisions?\",\"answer\":\"Models estimate the likelihood of obtaining consent using demographic, clinical, and contextual data, enabling clinicians to start preparatory activities earlier when consent probability is high.\"},{\"question\":\"What factors influence prediction accuracy according to the results?\",\"answer\":\"Prediction performance exceeds 80% overall and depends on included information about the relatives responsible for the decision, with higher accuracy reported for non-Italian donors.\"}]","Forecasting Consent in Organ Donation - Early Assessment of Machine-Learning Techniques | PDF",1785938774,25,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"forecasting-consent-in-organ-donation-early-assessment-of-machine-learning-techniques","",{"@graph":36,"@context":86},[37,54,69],{"@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/forecasting-consent-in-organ-donation-early-assessment-of-machine-learning-techniques/127418/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","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 problem does this study address in organ donation?","Question",{"text":76,"@type":77},"The study addresses the challenge of predicting whether consent for organ donation will be granted, which is crucial when consent must be obtained post-mortem from next-of-kin.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are the machine-learning models used to support early decisions?",{"text":81,"@type":77},"Models estimate the likelihood of obtaining consent using demographic, clinical, and contextual data, enabling clinicians to start preparatory activities earlier when consent probability is high.",{"name":83,"@type":74,"acceptedAnswer":84},"What factors influence prediction accuracy according to the results?",{"text":85,"@type":77},"Prediction performance exceeds 80% overall and depends on included information about the relatives responsible for the decision, with higher accuracy reported for non-Italian donors.","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":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":21,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]