[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126099-en":3,"doc-seo-126099-105":31,"detail-sidebar-cat-0-en-105":93},{"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},126099,5909887256941,"Levi","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",7,"Healthcare","Estimating Kidney Transplantation Donor-Recipient Compatibility Using Machine Learning","Rising prevalence of kidney-related diseases increases the need for effective donor–recipient matching to reduce strain on healthcare systems. Prior research shows machine-learning approaches can predict post-transplant survival. This work evaluates compatibility using a random survival forest model that predicts graft survival time from pre-transplantation metrics. The model is further used to assess factors affecting graft survival, while providing an in-depth study of random survival forests and survival analysis methods used in medical research.","Czech Technical University in Prague Faculty of Nuclear Sciences and Physical Engineering  \nEstimating Kidney Transplantation Donor-Recipient Compatibility Using Machine  \nLearning  \nOdhad kompatibility dárce a píjemce pro transplantaci ledvin pomocí strojového uení  \nBachelor’s Degree Project  \nAuthor: Matj Klouek  \nSupervisor: Ing. Tomáš Kouim  \nConsultant: Ing. Pavel Strachota , Ph.D.  \nAcademic year: 2022/2023  \nAcknowledgment:  \nI would like to thank Ing. Tomáš Kouˇrim for his expert guidance and express my gratitude to Ing. Pavel Strachota, Ph.D. for his assistance with the formal aspects of this project.  \nAuthor’s declaration:  \nI declare that this Bachelor’s Degree Project is entirely my own work and I have listed all the used sources in the bibliography.  \nPrague, August 2, 2023 Matj Klouek  \nNázev práce:  \nOdhad kompatibility dárce a píjemce pro transplantaci ledvin pomocí strojového uení  \nAutor: Matj Klouek  \nObor: Matematické inženýrství  \nZamˇeˇrení: Matematická informatika  \nDruh práce: Bakaláˇrská práce  \nVedoucí práce: Ing. Tomáš Kouˇrim, Mild Blue, s.r.o.  \nKonzultant: Ing. Pavel Strachota , Ph.D., Katedra matematiky FJFI ˇCVUT  \nAbstrakt: Se zvyšující se prevalencí onemocnní spojených s ledvinami je vylepšení procesu hledání vhodných pár˚u dárc˚u a pˇríjemc˚u zásadní pro zmírnní zátže, kterou pro zdravotnický systém pˇredstavují pacienti se selhávajícími ledvinami. Pˇredchozí studie ukázaly, jak mohou metody strojového uení býtpoužity pro predikování asu pˇrežití po transplantaci ledvin. V této studiije pro ohodnocení kompatibilitymezi dárci a pˇríjemci použit model náhodného pˇrežívacího lesa, který predikuje dobu pˇrežití na základjejich pre-transplantaních metrik. Model je také použit pro zkoumání r˚uzných faktor˚u ovlivˇnujícíchpˇrežití štpu. Souástí studie je také podrobné prozkoumání algoritm˚u, které stojí za modelem náhodnéhopˇrežívacího lesa, a analýzy pˇrežití, která se bžn využívá v medicínském výzkumu.  \nKlíˇcová slova: strojové uení, rozhodovací strom, náhodný les, transplantace ledvin, analýza pˇrežívání, náhodný pˇrežívací les  \nTitle:  \nEstimating Kidney Transplantation Donor-Recipient Compatibility Using Machine Learning  \nAuthor: Matj Klouek  \nAbstract: With the increasing prevalence of kidney-related diseases, the improvement of the donorrecipient match-making process becomes crucial for alleviating some of the burden placed on the healthcare system by patients with failing kidneys. Several other studies have already demonstrated how machine learning methods could be used for predicting survival time after renal transplantation. In this study, the random survival forest model is used to evaluate compatibility between donors and recipients by predicting their survival time based on their pre-transplantation metrics. The model is then also used to investigate the various factors influencing graft survival. The study also includes an in-depth examination of the algorithms behind the random survival forests model, as well as survival analysis, a field of statistics commonly used in medical research.  \nKey words: Machine learning, Decision tree, Random forest, Renal transplantation, Survival analysis, Random survival forest  \nContents  \nIntroduction 8  \n1 Machine Learning 10  \n1.1 General Overview of Machine Learning .......................... 10  \n1.1.1 Classification of Machine Learning Models .................... 10  \n1.2 Building a Machine Learning Model ............................ 11  \n1.2.1 Data Preprocessing ................................. 11  \n1.2.2 Evaluating Performance .............................. 13  \n1.2.3 Feature Selection .................................. 13  \n1.2.4 Hyperparameter Tuning .............................. 13  \n1.3 Decision Trees and Random Forests ............................ 14  \n1.3.1 Decision Trees ................................... 14  \n1.3.2 Ensemble Learning and Random Forests ..................... 16  \n2 Renal Transplantation 19  \n2.1 Chronic Kidney Di","cbCaid5JpxPvNmYJ","https://ap.wps.com/l/cbCaid5JpxPvNmYJ","pdf",1111859,6,1,54,"English","en",105,"# Introduction\n## Machine Learning\n## Renal Transplantation\n## Survival Analysis\n## Random Survival Forests\n## Data and Software Architecture\n## Model Training","[{\"question\":\"How does the study evaluate donor-recipient compatibility?\",\"answer\":\"It uses a random survival forest model to predict survival time based on pre-transplantation metrics of donors and recipients, then interprets the predicted survival as compatibility.\"},{\"question\":\"What model is used for survival prediction?\",\"answer\":\"The study applies the random survival forest model to estimate graft survival time from pre-transplant features.\"},{\"question\":\"Which additional topics are covered beyond the main model?\",\"answer\":\"The project includes an in-depth examination of the algorithms behind random survival forests and a survival analysis review, including methods commonly used in medical research.\"}]","Estimating Kidney Transplantation Donor-Recipient Compatibility Using Machine Learning | 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does the study evaluate donor-recipient compatibility?","Question",{"text":77,"@type":78},"It uses a random survival forest model to predict survival time based on pre-transplantation metrics of donors and recipients, then interprets the predicted survival as compatibility.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What model is used for survival prediction?",{"text":82,"@type":78},"The study applies the random survival forest model to estimate graft survival time from pre-transplant features.",{"name":84,"@type":75,"acceptedAnswer":85},"Which additional topics are covered beyond the main model?",{"text":86,"@type":78},"The project includes an in-depth examination of the algorithms behind random survival forests and a survival analysis review, including methods commonly used in medical 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