{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/104893"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/104893","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Deep learning survival analysis for clinical decision support in deceased donor kidney transplantation","abstract":"In deceased donor kidney transplantation, the decision to accept or decline an offer relies on a clinicians intuition and ability to digest complex information in order to maximize patient survival. Risks affecting patient survival post-KT must be balanced with the risks of remaining on the waitlist. These risks include mortality, graft failure, and becoming too sick to transplant. The allocation system today takes these risk into account by way of the KDPI and EPTS scores. While these scores are discriminative of patient survival they were built with an assumption of independence between risks and very few donor-recipient variables. Deep learning survival analysis can effectively handle competing risks and learn complex relationships between many more donor-recipient variables. We used DeepHit to assess the risk benefit associated with accepting a kidney offer or remaining on the waitlist. Our models achieved comparable, if not better performance in certain tasks, with other high performing models in the literature and revealed that decoupling competing risks led to increased clinical information gain. We show that comprehensively modeling competing risks using machine learning can achieve more granular, meaningful clinical risk analysis enabling more effective decision making in deceased donor kidney transplantation.","abstract_html":"In deceased donor kidney transplantation, the decision to accept or decline an offer relies on a clinicians intuition and ability to digest complex information in order to maximize patient survival. Risks affecting patient survival post-KT must be balanced with the risks of remaining on the waitlist. These risks include mortality, graft failure, and becoming too sick to transplant. The allocation system today takes these risk into account by way of the KDPI and EPTS scores. While these scores are discriminative of patient survival they were built with an assumption of independence between risks and very few donor-recipient variables. Deep learning survival analysis can effectively handle competing risks and learn complex relationships between many more donor-recipient variables. We used DeepHit to assess the risk benefit associated with accepting a kidney offer or remaining on the waitlist. Our models achieved comparable, if not better performance in certain tasks, with other high performing models in the literature and revealed that decoupling competing risks led to increased clinical information gain. We show that comprehensively modeling competing risks using machine learning can achieve more granular, meaningful clinical risk analysis enabling more effective decision making in deceased donor kidney transplantation.","abstract_has_math":false,"creators":["Ruales Rosero, Paul E."],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Koyejo, Sanmi"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-23T20:00:09Z","date_published":"2019-08-23T20:00:09Z","updated_at":"2026-07-22T22:24:42Z","subjects":["deep learning, kidney transplant, survival analysis, machine learning, srtr, mortality"],"languages":["en"],"rights":["Copyright 2019 Paul Ruales"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/104893","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Koyejo, Sanmi"]},{"key":"dc:creator","label":"Author","values":["Ruales Rosero, Paul E."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-08-23T20:00:09Z","2019-04-22","2019-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["deep learning, kidney transplant, survival analysis, machine learning, srtr, mortality"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Paul Ruales"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/104893"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In deceased donor kidney transplantation, the decision to accept or decline an offer relies on a clinicians intuition and ability to digest complex information in order to maximize patient survival. Risks affecting patient survival post-KT must be balanced with the risks of remaining on the waitlist. These risks include mortality, graft failure, and becoming too sick to transplant. The allocation system today takes these risk into account by way of the KDPI and EPTS scores. While these scores are discriminative of patient survival they were built with an assumption of independence between risks and very few donor-recipient variables. Deep learning survival analysis can effectively handle competing risks and learn complex relationships between many more donor-recipient variables. We used DeepHit to assess the risk benefit associated with accepting a kidney offer or remaining on the waitlist. Our models achieved comparable, if not better performance in certain tasks, with other high performing models in the literature and revealed that decoupling competing risks led to increased clinical information gain. We show that comprehensively modeling competing risks using machine learning can achieve more granular, meaningful clinical risk analysis enabling more effective decision making in deceased donor kidney transplantation.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-08-22 without embargo terms","The student, Paul Ruales Rosero, accepted the attached license on 2019-04-21 at 15:55.","The student, Paul Ruales Rosero, submitted this Thesis for approval on 2019-04-21 at 16:02.","This Thesis was approved for publication on 2019-04-22 at 10:22.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13808 on 2019-08-22 at 14:45:19","Made available in DSpace on 2019-08-23T20:00:09Z (GMT). No. of bitstreams: 3 RUALESROSERO-THESIS-2019.pdf: 2755970 bytes, checksum: dd3a0d7d95bde0a653d67d687e9005c5 (MD5) thesis.zip: 3704258 bytes, checksum: 39813df85029b18256724f7713a96220 (MD5) LICENSE.txt: 4215 bytes, checksum: e20c959c04f2b7e60b76e7c8c9133f6b (MD5) Previous issue date: 2019-04-22"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Deep learning survival analysis for clinical decision support in deceased donor kidney transplantation"]}]}],"canonical_facts":{"dc:contributor":["Koyejo, Sanmi"],"dc:creator":["Ruales Rosero, Paul E."],"dc:date":["2019-08-23T20:00:09Z","2019-04-22","2019-05"],"dc:description":["In deceased donor kidney transplantation, the decision to accept or decline an offer relies on a clinicians intuition and ability to digest complex information in order to maximize patient survival. Risks affecting patient survival post-KT must be balanced with the risks of remaining on the waitlist. These risks include mortality, graft failure, and becoming too sick to transplant. The allocation system today takes these risk into account by way of the KDPI and EPTS scores. While these scores are discriminative of patient survival they were built with an assumption of independence between risks and very few donor-recipient variables. Deep learning survival analysis can effectively handle competing risks and learn complex relationships between many more donor-recipient variables. We used DeepHit to assess the risk benefit associated with accepting a kidney offer or remaining on the waitlist. Our models achieved comparable, if not better performance in certain tasks, with other high performing models in the literature and revealed that decoupling competing risks led to increased clinical information gain. We show that comprehensively modeling competing risks using machine learning can achieve more granular, meaningful clinical risk analysis enabling more effective decision making in deceased donor kidney transplantation.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-08-22 without embargo terms","The student, Paul Ruales Rosero, accepted the attached license on 2019-04-21 at 15:55.","The student, Paul Ruales Rosero, submitted this Thesis for approval on 2019-04-21 at 16:02.","This Thesis was approved for publication on 2019-04-22 at 10:22.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13808 on 2019-08-22 at 14:45:19","Made available in DSpace on 2019-08-23T20:00:09Z (GMT). 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