Predicting 30-day mortality in severely injured elderly patients with trauma in Korea using machine learning algorithms: a retrospective study
We showed that machine learning models, especially XGBoost, can be used to predict 30-day mortality in severely injured elderly patients with trauma. Prognostic tools utilizing these models are helpful for physicians to evaluate the risk of mortality in elderly patients with severe trauma.
Authors
- Jin Bong Ye (ORCID: https://orcid.org/0000-0002-5597-4502)
- Jonghee Han (ORCID: https://orcid.org/0000-0001-7370-4197)
- Junepill Seok (ORCID: https://orcid.org/0000-0002-9010-6495)
- Jin Suk Lee (ORCID: https://orcid.org/0000-0002-6694-4683)
- Young Hoon Sul (ORCID: https://orcid.org/0000-0003-3184-3396)
- Hong Rye Kim (ORCID: https://orcid.org/0000-0003-3149-5344)
- Jin Young Lee (ORCID: https://orcid.org/0000-0002-4629-0934)
- Soo Young Yoon (ORCID: https://orcid.org/0000-0001-9264-1371)
- Se Heon Kim (ORCID: https://orcid.org/0000-0002-1864-4096)
Institutions
- Chungbuk National University (KR)
- Chungbuk National University Hospital (KR)
Publication Details
- Journal
- Journal of Trauma and Injury
- Published
- 2024-08-20
- DOI
- https://doi.org/10.20408/jti.2024.0024
- Citations
- 3
- Primary Topic
- Trauma and Emergency Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 2.07