Risk Factor Assessment and Predictive Modeling for Ventilator‐Associated Pneumonia: Design and Clinical Implementation of an Artificial Intelligence‐Enhanced Early Detection Framework Using Multisource Data Analytics
The MM-HNN demonstrates the feasibility of accurate, interpretable VAP risk prediction through multimodal data integration. This artificial intelligence framework provides a clinically actionable tool for dynamic risk assessment, enabling preemptive interventions and improved antibiotic stewardship.
Authors
- Jiaxin Li (ORCID: https://orcid.org/0000-0002-4790-3173)
- Jichao Chen (ORCID: https://orcid.org/0000-0003-2749-8550)
- Yuwei Cao (ORCID: https://orcid.org/0000-0003-2284-4223)
- Y. Wang (ORCID: https://orcid.org/0009-0006-0037-7248)
- Wailong Zou
- Jia Zhang (ORCID: https://orcid.org/0000-0003-2148-0923)
- Yulin Li (ORCID: https://orcid.org/0000-0001-6907-5594)
- Zhe Zhang (ORCID: https://orcid.org/0000-0001-8407-3167)
- Xin Zhang (ORCID: https://orcid.org/0000-0003-0314-5718)
- Xinjun Zhang (ORCID: https://orcid.org/0000-0002-8717-354X)
- Rui Yang (ORCID: https://orcid.org/0000-0001-5049-3091)
- S.T. Dai
Institutions
- Aerospace Center Hospital (CN)
- Beijing Hospital (CN)
Publication Details
- Journal
- The Clinical Respiratory Journal
- Published
- 2025-11-29
- DOI
- https://doi.org/10.1111/crj.70144
- Primary Topic
- Nosocomial Infections in ICU
- Type
- article
- Field-Weighted Citation Impact
- 0.00