Interpretable machine learning based comorbidity specific mortality risk score for bloodstream infections
The BHScore enables the early identification of high-risk BSI patients with diverse comorbidities using straightforward clinical indicators, facilitating timely and targeted interventions to reduce mortality.
Authors
- Meng Tang (ORCID: https://orcid.org/0000-0002-1328-1011)
- Fei Mu (ORCID: https://orcid.org/0000-0001-5706-8864)
- Jinyi Zhao (ORCID: https://orcid.org/0000-0002-1561-2638)
- Dahua Wang (ORCID: https://orcid.org/0000-0003-2828-2532)
- Jingwen Wang
- Zhen Yan (ORCID: https://orcid.org/0000-0001-6068-9645)
- Yamei Li (ORCID: https://orcid.org/0000-0002-1059-7950)
- Chen Cui (ORCID: https://orcid.org/0000-0003-2932-6613)
- Kexin Sun
- Rui Gong (ORCID: https://orcid.org/0009-0005-3690-3249)
Institutions
- Xuzhou Medical College (CN)
- Northwest University (CN)
- Xuzhou No.1 People's Hospital (CN)
- Xijing Hospital (CN)
- First Hospital of Xi'an (CN)
- Air Force Medical University (CN)
Publication Details
- Journal
- Journal of Infection
- Published
- 2025-11-20
- DOI
- https://doi.org/10.1016/j.jinf.2025.106656
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China