Significant adverse prognostic events in patients with urosepsis: a machine learning based model development and validation study
ML demonstrates strong prognostic capability in urosepsis, with the SHAP method providing clinically intuitive explanations of model predictions. This enables clinicians to identify critical prognostic factors and personalize treatments. While our model achieved high predictive accuracy, its retrospective derivation from a single-center database necessitates external validation in diverse populations, which should be addressed through future prospective multicenter studies to establish clinical generalizability.
Authors
- Jia Wang (ORCID: https://orcid.org/0000-0003-2308-2259)
- Xianyao Wan (ORCID: https://orcid.org/0000-0002-3178-4982)
- Yutuo Wei (ORCID: https://orcid.org/0000-0002-6922-928X)
- Shuo Yang (ORCID: https://orcid.org/0000-0002-4710-8671)
- Wanqing Xu
- Congfeng Zhang (ORCID: https://orcid.org/0009-0008-3773-3386)
Institutions
- Dalian Medical University (CN)
- Wuhan Wudong Hospital (CN)
- Second Affiliated Hospital of Dalian Medical University (CN)
- First Affiliated Hospital of Dalian Medical University (CN)
- First Affiliated Hospital of Zhengzhou University (CN)
Publication Details
- Journal
- Frontiers in Cellular and Infection Microbiology
- Published
- 2025-08-08
- DOI
- https://doi.org/10.3389/fcimb.2025.1623109
- Citations
- 4
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 5.42