Predictive Modeling of Long-Term Care Needs in Traumatic Brain Injury Patients Using Machine Learning
Random Forest, with an 11-feature combination, provided clinically meaningful predictive capability, offering early insights into long-term care trends for TBI patients. This model supports proactive planning for institutional or RCW resources, addressing a critical yet often overlooked aspect of TBI care.
Authors
- Tee-Tau Eric Nyam
- Kuan-Chi Tu
- Jen-Chieh Liao (ORCID: https://orcid.org/0000-0002-8521-087X)
- Che-Chuan Wang (ORCID: https://orcid.org/0009-0000-7928-0882)
- Jinn‐Rung Kuo (ORCID: https://orcid.org/0000-0002-7820-8050)
- Nai-Ching Chen (ORCID: https://orcid.org/0000-0002-8534-8705)
- Chung‐Feng Liu (ORCID: https://orcid.org/0000-0001-6698-0273)
Institutions
- National Sun Yat-sen University (TW)
- Chi Mei Medical Center (TW)
- Chia Nan University of Pharmacy and Science (TW)
Publication Details
- Journal
- Diagnostics
- Published
- 2024-12-25
- DOI
- https://doi.org/10.3390/diagnostics15010020
- Citations
- 2
- Primary Topic
- Trauma and Emergency Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 1.38