Development and Validation of a Dynamic Real-Time Risk Prediction Model for Intensive Care Units Patients Based on Longitudinal Irregular Data: Multicenter Retrospective Study
The TBAL model offers a robust, interpretable, and generalizable solution for dynamic real-time mortality risk prediction in ICU patients. Its ability to adapt to irregular temporal patterns and to provide hourly updated predictions positions it as a promising decision-support tool. Future work should validate its utility in prospective clinical trials and investigate its integration into real-world ICU workflows to enhance patient outcomes.
Authors
- Jiawei Luo (ORCID: https://orcid.org/0000-0003-1617-3224)
- Xiaobo Zhou (ORCID: https://orcid.org/0000-0001-7191-6495)
- Shixin Huang (ORCID: https://orcid.org/0009-0001-7931-7056)
- Zhuo Zheng
- Lan Lan
- Xiaoyan Yang (ORCID: https://orcid.org/0000-0003-3293-6201)
- Lei Du (ORCID: https://orcid.org/0000-0001-9505-085X)
- Yingchao Zhu (ORCID: https://orcid.org/0000-0003-2945-0163)
Institutions
- Sichuan University (CN)
- West China Medical Center of Sichuan University (CN)
- Beijing Tian Tan Hospital (CN)
- West China Hospital of Sichuan University (CN)
- The University of Texas Health Science Center (US)
- First People's Hospital of Chongqing (CN)
- The University of Texas Health Science Center at Houston (US)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-04-23
- DOI
- https://doi.org/10.2196/69293
- Citations
- 6
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 14.89