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Predictive Modeling of Long-Term Care Needs in Traumatic Brain Injury Patients Using Machine Learning
Tee-Tau Eric Nyam1,2, Kuan-Chi Tu1, Nai-Ching Chen3
1Department of Neurosurgery, Chi Mei Medical Center, Tainan 711, Taiwan.
Diagnostics (Basel, Switzerland)
|January 11, 2025
Summary
Predicting long-term care needs for traumatic brain injury (TBI) patients is crucial. Machine learning models, particularly Random Forest, can effectively forecast TBI patient prognoses for better resource allocation.
Area of Science:
- Neurology
- Medical Informatics
- Machine Learning in Healthcare
Background:
- Traumatic brain injury (TBI) research often overlooks long-term care needs.
- Patients requiring institutional or Respiratory Care Ward (RCW) support post-TBI are a critical, understudied group.
Purpose of the Study:
- To develop and validate machine learning models for predicting long-term care prognosis in TBI patients.
- To address the gap in understanding long-term care requirements for TBI survivors.
Main Methods:
- Retrospective analysis of 2020 TBI patients' electronic medical records.
- Utilized 44 features and four machine learning models (XGBoost, Random Forest, LightGBM).
- Evaluated predictive performance using AUC-ROC, DeLong test, and SHAP analysis.
Main Results:
- 236 patients (11.68%) transferred to long-term care.
- XGBoost (27 features) achieved the highest AUC (0.823), followed by Random Forest (11 features, AUC 0.817).
- SHAP analysis confirmed feature importance consistency across top models.
Conclusions:
- Random Forest with 11 features offers clinically meaningful prediction of long-term care needs.
- This model aids proactive planning for institutional and RCW resources for TBI patients.
