[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122419-en":3,"doc-seo-122419-105":29,"detail-sidebar-cat-0-en-105":89},{"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":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},122419,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",7,"Healthcare","Addressing Artificial Intelligence Gaps in Transplant Medicine - A Machine Learning Solution","Artificial intelligence in transplant medicine offers value for organ matching and rejection prediction, yet personalized immunosuppression remains constrained by drug dosing optimization, patient-specific data integration, and practical clinical deployment. A systematic review of research from 2010–2024 highlights reliance on static predictors, rare multi-omics incorporation for metabolism and immune response modeling, and limited generalizability due to small single-center datasets. To close these gaps, a longitudinal machine learning framework is proposed to integrate EHRs, pharmacokinetics, genomics, and biomarkers for adaptive individualized dosing recommendations.","ADDRESSING ARTIFICIAL INTELLIGENCE GAPS IN TRANSPLANT MEDECINE:  \nA MACHINE LEARNING SOLUTION  \nScevenels Laura1, Bogdanov Alan1, Topor Boris1  \n1Department of Anatomy and Clinical Anatomy, Nicolae Testemitanu State University of Medicine and Pharmacy, Chisinau, the Republic of Moldova.  \nIntroduction: Artificial intelligence (AI) shows promise in transplant medicine, particularly in organ matching and rejection prediction. However, gaps remain in personalized immunosuppression, including optimal drug dosing, patient-specific data integration, and clinical implementation. This study identifies these gaps and proposes a machine learning model to optimize immunosuppressive therapy.  \nMaterial and Methods: A systematic review was conducted using PubMed, Scopus, and Web of Science from 2010 to 2024 with keywords: \"Artificial Intelligence,\" \"Transplant Medicine,\" \"Rejection Prediction,\" and \"Patient Care Optimization.\" Studies discussing AI applications in rejection prediction or patient care in organ transplantation were included. Data on study design, AI methods, outcomes, and limitations were extracted. Findings show most AI models rely on static predictors and fail to adapt to real-time changes like infections and inflammation. Multi-omics data, crucial for drug metabolism and immune response, are rarely integrated, reducing accuracy. Generalizability is also limited, as most models are trained on small, single-center datasets further reducing accuracy. To address these gaps, we propose a machine learning model using longitudinal transplant data. It will integrate electronic health records, pharmacokinetics, genomics, and biomarkers to predict individualized dosing. Recurrent neural networks or transformer-based architectures will update recommendations based on patient-specific responses. Model performance will be validated using real-world clinical data and benchmarked against traditional dosing protocols.  \nResults: The review included 68 articles, with 14 meeting inclusion criteria. While AI has been applied to organ matching and rejection prediction, no existing models provide real-time, patient-specific immunosuppression adjustments, impacting patient outcomes. The proposed model aims to bridge this gap, potentially reducing rejection rates and improving outcomes.  \nConclusions: This study identifies deficiencies in AI-driven immunosuppression management, particularly in real-time dose adjustments, multi-omics integration, and model generalizability. The proposed machine learning framework seeks to create an adaptive, personalized dosing system to enhance transplant outcomes and minimize rejection risks.  \nKeywords: Artificial Intelligence; Transplant Medicine; Personalized Immunosuppression; Machine Learning; Multi-Omics Data.","cbCail9UYouFd7ob","https://ap.wps.com/l/cbCail9UYouFd7ob","pdf",230480,1,"English","en",105,"# Introduction\n# Material and Methods\n# Results\n# Conclusions\n# Keywords","[{\"question\":\"What gaps in AI-based transplant immunosuppression are identified?\",\"answer\":\"The study highlights gaps in real-time, patient-specific immunosuppression adjustment, optimal drug dosing, patient data integration, multi-omics incorporation, and model generalizability.\"},{\"question\":\"How does the proposed machine learning solution address these limitations?\",\"answer\":\"It uses longitudinal transplant data and integrates electronic health records, pharmacokinetics, genomics, and biomarkers. Recurrent or transformer-based architectures update recommendations based on patient-specific responses.\"},{\"question\":\"What were the key findings from the systematic review?\",\"answer\":\"The review identified 68 articles, with 14 meeting inclusion criteria. Existing AI models address matching and rejection prediction, but none provide real-time, patient-specific immunosuppression changes.\"}]","Addressing Artificial Intelligence Gaps in Transplant Medicine - A Machine Learning Solution | PDF",1785810526,3,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":27},"addressing-artificial-intelligence-gaps-in-transplant-medicine-a-machine-learning-solution","",{"@graph":35,"@context":83},[36,52,66],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":28},"https://docshare.wps.com/document/healthcare/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/addressing-artificial-intelligence-gaps-in-transplant-medicine-a-machine-learning-solution/122419/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"What gaps in AI-based transplant immunosuppression are identified?","Question",{"text":73,"@type":74},"The study highlights gaps in real-time, patient-specific immunosuppression adjustment, optimal drug dosing, patient data integration, multi-omics incorporation, and model generalizability.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"How does the proposed machine learning solution address these limitations?",{"text":78,"@type":74},"It uses longitudinal transplant data and integrates electronic health records, pharmacokinetics, genomics, and biomarkers. Recurrent or transformer-based architectures update recommendations based on patient-specific responses.",{"name":80,"@type":71,"acceptedAnswer":81},"What were the key findings from the systematic review?",{"text":82,"@type":74},"The review identified 68 articles, with 14 meeting inclusion criteria. Existing AI models address matching and rejection prediction, but none provide real-time, patient-specific immunosuppression changes.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":90},[91,95,99,103,108,113,116,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":92,"show_sort_weight":93,"slug":94},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":96,"show_sort_weight":97,"slug":98},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":45,"category_name":100,"show_sort_weight":101,"slug":102},"Exam",70,"exam",{"id":104,"doc_module":4,"doc_module_name":45,"category_name":105,"show_sort_weight":106,"slug":107},5,"Comic",60,"comic",{"id":109,"doc_module":4,"doc_module_name":45,"category_name":110,"show_sort_weight":111,"slug":112},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":114,"slug":115},40,"healthcare",{"id":117,"doc_module":4,"doc_module_name":45,"category_name":118,"show_sort_weight":119,"slug":120},8,"Research & Report",30,"research-report",{"id":122,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":104,"slug":136},19,"General","general"]