Explainable machine learning model for predicting septic shock in critically sepsis patients based on coagulation indexes: A multicenter cohort study
The SVM algorithm may be superior to other ML and traditional statistical algorithms for predicting septic shock. Physicians can better understand the reliability of the predictive model by Shapley additive explanations value analysis.
Authors
- Jingchun Song (ORCID: https://orcid.org/0000-0001-5869-5077)
- Qingbo Zeng (ORCID: https://orcid.org/0000-0002-0742-125X)
- Nian-Qing Zhang
- Longping He (ORCID: https://orcid.org/0000-0002-4307-0733)
- Qing-Wei Lin
- Lin-Cui Zhong
- Ye Zhou (ORCID: https://orcid.org/0009-0005-6638-0303)
- En-Lan Peng
Institutions
- Nanchang University (CN)
- Second Affiliated Hospital of Nanchang University (CN)
- Second Hospital of Nanchang (CN)
- First Affiliated Hospital of Nanchang University (CN)
- Anyang Hospital of Traditional Chinese Medicine (CN)
Publication Details
- Journal
- Chinese Journal of Traumatology
- Published
- 2025-03-29
- DOI
- https://doi.org/10.1016/j.cjtee.2024.08.012
- Citations
- 1
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- National Key Research and Development Program of China