Enhancing heatstroke prediction accuracy with interpretable machine learning: a multi-center data-driven approach
The ML model we developed demonstrates robust predictive capabilities for heatstroke, potentially aiding clinicians in the identification and management of patients at elevated risk.
Authors
- Jingchun Song (ORCID: https://orcid.org/0000-0001-5869-5077)
- Nianqing Zhang
- Lincui Zhong (ORCID: https://orcid.org/0000-0001-5463-8745)
- Qingbo Zeng (ORCID: https://orcid.org/0000-0002-0742-125X)
- Xingping Deng
- Longping He (ORCID: https://orcid.org/0000-0002-4307-0733)
- Qingwei Lin
Institutions
- 82th Hospital of Pla (CN)
- Nanchang Center for Disease Control and Prevention (CN)
- Anyang Hospital of Traditional Chinese Medicine (CN)
Publication Details
- Journal
- PeerJ
- Published
- 2025-11-14
- DOI
- https://doi.org/10.7717/peerj.20377
- Primary Topic
- Thermoregulation and physiological responses
- Type
- article
- Field-Weighted Citation Impact
- 0.00