Supervised Machine Learning Models Predicting Postoperative Low Cardiac Output Syndrome In Neonates
This study demonstrates that the supervised machine learning models can accurately predict LCOS in neonates, offering high interpretability. The findings support further validation in multicenter settings and integration into clinical workflows to enhance postoperative critical cardiac care neonates.
Authors
- Izzet Turkalp Akbasli (ORCID: https://orcid.org/0000-0003-3055-7355)
- Xiao Feng Wang (ORCID: https://orcid.org/0000-0001-5966-6673)
- Bradley S. Marino (ORCID: https://orcid.org/0000-0002-5384-5432)
- Animesh Tandon (ORCID: https://orcid.org/0000-0001-9769-8801)
- Orkun Baloğlu (ORCID: https://orcid.org/0000-0001-5629-7788)
- Ayse Morca
- Samir Latifi
- Alex Klaben
Institutions
- Cleveland Clinic (US)
- Cleveland Clinic Lerner College of Medicine (US)
Publication Details
- Journal
- Critical Care Explorations
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1097/cce.0000000000001327
- Primary Topic
- Congenital Heart Disease Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00