A Machine Learning–Based Prediction Model for Acute Kidney Injury in Patients With Community-Acquired Pneumonia: Multicenter Validation Study
The model described in this study is the first multicenter-validated AKI prediction model that accurately predicts CAP-AKI during hospitalization. The web-based prediction platform embedded with the DF model serves as a user-friendly tool for early identification of high-risk patients.
Authors
- Changchun Cao (ORCID: https://orcid.org/0000-0003-1992-4257)
- Xueting Chen
- Caimei Chen (ORCID: https://orcid.org/0000-0003-4954-954X)
- Huiping Kong (ORCID: https://orcid.org/0000-0002-4384-1251)
- Dawei Chen (ORCID: https://orcid.org/0000-0001-5088-6565)
- Xin Hua Wan (ORCID: https://orcid.org/0000-0001-5226-4444)
- Hao Zhang (ORCID: https://orcid.org/0000-0001-8055-7789)
- Qing Sun
- Mengqing Ma (ORCID: https://orcid.org/0000-0003-4402-7988)
- Xia Du (ORCID: https://orcid.org/0009-0005-9090-6264)
- Fan Li (ORCID: https://orcid.org/0000-0002-7853-3195)
Institutions
- Xintai People's Hospital (CN)
- Wuxi People's Hospital (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2024-12-19
- DOI
- https://doi.org/10.2196/51255
- Citations
- 5
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.93
Funders
- National Science Foundation
- Jiangsu Commission of Health
- National Natural Science Foundation of China