Development of an Explainable Machine Learning Model to Predict Mortality Risk in Sepsis Patients: insights from a real-world clinical data
Local explanation methods can interpret how ML models make predictions for individual outcomes. Global interpretation techniques help reveal how ML models respond across the entire feature landscape.
Authors
- Fengwei Liang (ORCID: https://orcid.org/0009-0006-7636-6844)
- Xiaohan Hu (ORCID: https://orcid.org/0000-0002-5608-2864)
- Honglin Wang (ORCID: https://orcid.org/0000-0002-6824-4269)
- Dandan Tang (ORCID: https://orcid.org/0000-0001-9963-1301)
- Jie Wang (ORCID: https://orcid.org/0000-0002-0827-8483)
- Xingli Gu
- Yuanyuan Jin (ORCID: https://orcid.org/0000-0003-3583-5668)
Institutions
- Xinjiang Medical University (CN)
- First Affiliated Hospital of Xinjiang Medical University (CN)
Publication Details
- Journal
- Shock
- Published
- 2025-10-27
- DOI
- https://doi.org/10.1097/shk.0000000000002744
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00