Performance of Supervised Machine Learning Models for Cardiac Surgery-Associated Acute Kidney Injury in Children: Multicenter Retrospective Cohort Study, 2019–2022
This analysis of a retrospective multicenter dataset shows that external performance of ML models vary, highlighting challenges in generalizability, which may be due to center-based differences in practice.
Authors
- Izzet Turkalp Akbasli (ORCID: https://orcid.org/0000-0003-3055-7355)
- Katja M. Gist (ORCID: https://orcid.org/0000-0002-7427-670X)
- Jamie Penk (ORCID: https://orcid.org/0000-0001-8465-2206)
- Bradley S. Marino (ORCID: https://orcid.org/0000-0002-5384-5432)
- Orkun Baloğlu (ORCID: https://orcid.org/0000-0001-5629-7788)
- Ayse Morca
- Samir Latifi
Institutions
- Cleveland Clinic (US)
- Children's Hospital Colorado (US)
- Lurie Children's Hospital (US)
- University of Colorado Denver (US)
Publication Details
- Journal
- Pediatric Critical Care Medicine
- Published
- 2025-11-10
- DOI
- https://doi.org/10.1097/pcc.0000000000003857
- Primary Topic
- Acute Kidney Injury Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00