On-scene machine learning prediction model for massive transfusion in trauma and its association with in-hospital mortality
A prehospital ensemble learning model to predict MT was developed and validated, and its predictions were significantly associated with in-hospital mortality. However, this study is limited by the inclusion of a single ethnicity, and future research needs to integrate data from multiple populations to enhance generalizability.
Authors
- Jaehyeong Cho (ORCID: https://orcid.org/0009-0008-7892-3741)
- Wan Shin Kim (ORCID: https://orcid.org/0000-0001-7151-2992)
- Kyounghwan Kim (ORCID: https://orcid.org/0000-0002-7411-4794)
- J. S. Oh
- Junepill Seok
- Soeun Kim (ORCID: https://orcid.org/0009-0009-5874-417X)
- Wu Seong Kang
- Jinseok Lee
- Hyunjee Kim
- Sooji Lee
- Jiyoung Hwang
- Dong Keon Yon
- Byungchul Yu
- Seung Ha Hwang
Institutions
- Gachon University (KR)
- Chungbuk National University (KR)
- Kyung Hee University (KR)
- Gachon University Gil Medical Center (KR)
- Chonnam National University Hospital (KR)
- Kyung Hee University Medical Center (KR)
- Cheju Halla General Hospital (KR)
- CHA University (KR)
- Chungbuk National University Hospital (KR)
Publication Details
- Journal
- BJS Open
- Published
- 2025-12-09
- DOI
- https://doi.org/10.1093/bjsopen/zraf167
- Primary Topic
- Trauma, Hemostasis, Coagulopathy, Resuscitation
- Type
- article
- Field-Weighted Citation Impact
- 0.00