1021 - Integrating Socioeconomic Status into Machine Learning Models of Traumatic Brain Injury Rehabilitation Outcomes: A Cost-Benefit Perspective

To assess the limitations of existing post–traumatic brain injury recovery models and evaluate the potential benefits of incorporating socioeconomic data through a proposed SES-Aware Functional Recovery Predictor (SAFR-P), highlighting its anticipated impact on prognostic accuracy, resource allocation, and equity in rehabilitation outcomes.

Design: A conceptual SES-Aware Functional Recovery Predictor (SAFR-P) model was outlined as a modular, ensemble machine learning system capable of processing both clinical and socioeconomic data streams. Clinical features would include Glasgow Coma Scale (GCS) scores, neuroimaging findings, and comorbidities, while socioeconomic inputs would capture insurance type, Area Deprivation Index (ADI), education level, and employment status. A cost–benefit framework was applied to evaluate the potential utility of integrating socioeconomic data into predictive models of post-TBI recovery.

Results:

Evidence from existing literature suggests that models excluding socioeconomic factors risk underestimating disparities and misclassifying patients. Cost–benefit analysis indicates that incorporating SES features could improve prognostic accuracy, facilitate more equitable rehabilitation planning, and yield long-term savings through more efficient allocation of rehabilitation resources. Prior studies highlight that socioeconomic indicators such as insurance type and ADI are strong predictors of access, discharge disposition, and functional outcomes, underscoring their potential importance within predictive modeling.

Conclusions:

The proposed SAFR-P framework represents a rationale for developing more inclusive and equitable prognostic tools in TBI rehabilitation. Incorporating socioeconomic data alongside clinical measures could enhance outcome prediction, guide early identification of high-risk patients, and support both clinical decision-making and policy initiatives aimed at reducing systemic disparities in rehabilitation access and recovery.