Predictive models for posttransplant diabetes mellitus in kidney transplant recipients using machine learning and deep learning approach: a nationwide cohort study from South Korea
The XGBoost model demonstrated the best performance for predicting PTDM within 1 year, offering an accurate tool for early identification and personalized care of high-risk KTRs for PTDM.
Authors
- Sangwoong Kim (ORCID: https://orcid.org/0009-0004-0818-1261)
- Jong Cheol Jeong (ORCID: https://orcid.org/0000-0003-0301-7644)
- Hye Eun Yoon (ORCID: https://orcid.org/0000-0002-6347-7282)
- Seoyoung Choi (ORCID: https://orcid.org/0009-0009-0286-1510)
- Sejoong Kim (ORCID: https://orcid.org/0000-0002-7238-9962)
- Hyejin Mo (ORCID: https://orcid.org/0000-0002-5383-0863)
- Jaeseok Yang (ORCID: https://orcid.org/0000-0002-5378-7797)
- Myoung Soo Kim (ORCID: https://orcid.org/0000-0002-8975-8381)
- Yu Ho Lee (ORCID: https://orcid.org/0000-0001-5231-0551)
- Jeong‐Hoon Lee (ORCID: https://orcid.org/0000-0002-0315-2080)
- Mi Ryung Pyo
Institutions
- Seoul National University (KR)
- Yonsei University (KR)
- Seoul National University Bundang Hospital (KR)
- SNUH SMG-SNU Boramae Medical Center (KR)
- The Catholic University of Korea Incheon St. Mary's Hospital (KR)
- CHA University Bundang Medical Center (KR)
- Myongji Hospital (KR)
- CHA University (KR)
- Catholic University of Korea (KR)
Publication Details
- Journal
- Kidney Research and Clinical Practice
- Published
- 2025-01-09
- DOI
- https://doi.org/10.23876/j.krcp.24.113
- Citations
- 2
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 5.88
Funders
- Korea Health Industry Development Institute
- National Institutes of Health