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Prediction tool for renal adaptation after living kidney donation using interpretable machine learning
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dc.contributor.author | 유재용 | - |
dc.date.accessioned | 2024-05-30T07:00:38Z | - |
dc.date.available | 2024-05-30T07:00:38Z | - |
dc.date.issued | 2023-07 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/199508 | - |
dc.description.abstract | Introduction: Post-donation renal outcomes are a crucial issue for living kidney donors considering young donors' high life expectancy and elderly donors' comorbidities that affect kidney function. We developed a prediction model for renal adaptation after living kidney donation using interpretable machine learning.Methods: The study included 823 living kidney donors who underwent nephrectomy in 2009-2020. AutoScore, a machine learning-based score generator, was used to develop a prediction model. Fair and good renal adaptation were defined as post-donation estimated glomerular filtration rate (eGFR) of & GE; 60 mL/min/1.73 m(2) and & GE; 65% of the pre-donation values, respectively.Results: The mean age was 45.2 years; 51.6% were female. The model included pre-donation demographic and laboratory variables, GFR measured by diethylenetriamine pentaacetate scan, and computed tomography kidney volume/body weight of both kidneys and the remaining kidney. The areas under the receiver operating characteristic curve were 0.846 (95% confidence interval, 0.762-0.930) and 0.626 (0.541-0.712), while the areas under the precision-recall curve were 0.965 (0.944-0.978) and 0.709 (0.647-0.788) for fair and good renal adaptation, respectively. An interactive clinical decision support system was developed.(1)Conclusion: The prediction tool for post-donation renal adaptation showed good predictive capability and may help clinical decisions through an easy-to-use web-based application. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | Frontiers Media S.A. | - |
dc.relation.isPartOf | FRONTIERS IN MEDICINE | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Prediction tool for renal adaptation after living kidney donation using interpretable machine learning | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) | - |
dc.contributor.googleauthor | Junseok Jeon | - |
dc.contributor.googleauthor | Jae Yong Yu | - |
dc.contributor.googleauthor | Yeejun Song | - |
dc.contributor.googleauthor | Weon Jung | - |
dc.contributor.googleauthor | Kyungho Lee | - |
dc.contributor.googleauthor | Jung Eun Lee | - |
dc.contributor.googleauthor | Wooseong Huh | - |
dc.contributor.googleauthor | Won Chul Cha | - |
dc.contributor.googleauthor | Hye Ryoun Jang | - |
dc.identifier.doi | 10.3389/fmed.2023.1222973 | - |
dc.contributor.localId | A06594 | - |
dc.relation.journalcode | J03762 | - |
dc.identifier.eissn | 2296-858X | - |
dc.identifier.pmid | 37521345 | - |
dc.subject.keyword | AutoScore | - |
dc.subject.keyword | kidney transplantation | - |
dc.subject.keyword | living donor | - |
dc.subject.keyword | machine learning | - |
dc.subject.keyword | renal adaptation | - |
dc.contributor.alternativeName | Yu, Jae Yong | - |
dc.contributor.affiliatedAuthor | 유재용 | - |
dc.citation.volume | 10 | - |
dc.citation.startPage | 1222973 | - |
dc.identifier.bibliographicCitation | FRONTIERS IN MEDICINE, Vol.10 : 1222973, 2023-07 | - |
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