Machine learning‐based prediction of bleeding risk in extracorporeal membrane oxygenation patients using transfusion as a surrogate marker
The LightGBM and Random Forest models effectively predict bleeding complications in patients with ECMO, using RBC transfusion as a surrogate marker. This tool can support early identification of high-risk patients and improve overall transfusion management.
Authors
- Kiyomitsu Fukaguchi (ORCID: https://orcid.org/0000-0003-2262-1898)
- Motohiro Mizuno (ORCID: https://orcid.org/0000-0002-1712-9308)
- Yoshihito Machida
- Shin Ishii (ORCID: https://orcid.org/0000-0001-9385-8230)
- Tadashi Kamio (ORCID: https://orcid.org/0000-0002-8987-0103)
- Masaru Ikegami
- Hayato Tajima
Institutions
- Jichi Medical University (JP)
- Terumo (Japan) (JP)
- Shonan Kamakura General Hospital (JP)
- Jichi Medical University Saitama Medical Center (JP)
Publication Details
- Journal
- Transfusion
- Published
- 2025-04-25
- DOI
- https://doi.org/10.1111/trf.18261
- Citations
- 2
- Primary Topic
- Mechanical Circulatory Support Devices
- Type
- article
- Field-Weighted Citation Impact
- 1.35