A Machine Learning Model Based on CT Imaging Metrics and Clinical Features to Predict the Risk of Hospital-Acquired Pneumonia After Traumatic Brain Injury
We successfully developed and validated a high-performance ML algorithm to assess the risk of HAP in TBI patients. The dynamic nomogram provides a practical tool for real-time risk assessment, potentially improving clinical outcomes by aiding in early intervention and personalized patient management.
Authors
- Qiangqiang Feng
- Jiayin Wang (ORCID: https://orcid.org/0000-0002-3862-6557)
- Yiming Zhuang (ORCID: https://orcid.org/0000-0001-9482-5289)
- Hongzhi Gao (ORCID: https://orcid.org/0000-0002-5821-2407)
- Yong Wang (ORCID: https://orcid.org/0000-0001-8511-0097)
- Shaojie Li (ORCID: https://orcid.org/0009-0008-2560-0017)
- Baofang Wu
- Weizhi Qiu
Institutions
- Fujian Medical University (CN)
- Second Affiliated Hospital of Fujian Medical University (CN)
Publication Details
- Journal
- Infection and Drug Resistance
- Published
- 2024-09-01
- DOI
- https://doi.org/10.2147/idr.s473825
- Citations
- 7
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 2.71