Machine Learning-Based Prediction Model for Multidrug-Resistant Organisms Infections: Performance Evaluation and Interpretability Analysis
This study establishes an interpretable prediction framework integrating RF algorithms with SHAP explainability, balancing predictive accuracy with clinical transparency. The model's dynamic visualization capabilities support individualized risk assessment and evidence-based antimicrobial stewardship. Integration into hospital information systems with real-time dashboards could enhance early intervention strategies.
Authors
- Pei Sun (ORCID: https://orcid.org/0009-0003-4948-5555)
- Linping Shang (ORCID: https://orcid.org/0000-0002-4009-844X)
- Wenting Zhao (ORCID: https://orcid.org/0000-0002-0436-0308)
- Wei Li (ORCID: https://orcid.org/0000-0002-2305-2642)
Institutions
- Shanxi Medical University (CN)
- Changzhi Medical College (CN)
- First Hospital of Shanxi Medical University (CN)
Publication Details
- Journal
- Infection and Drug Resistance
- Published
- 2025-05-01
- DOI
- https://doi.org/10.2147/idr.s459830
- Citations
- 1
- Primary Topic
- Yersinia bacterium, plague, ectoparasites research
- Type
- article
- Field-Weighted Citation Impact
- 1.82
Funders
- Shanxi Medical University
- Division of Graduate Education