Srikanchana Rujirutana, Samuel David, Powell Jacob, Pickett Treven, DeGraba Thomas, Sours Rhodes Chandler
National Intrepid Center of Excellence (NICoE), Walter Reed National Military Medical Center, Bethesda, MD, USA.
Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
Ann Biomed Eng. 2025 Sep 23. doi: 10.1007/s10439-025-03853-5.
The aim of this research was to assess the potential for machine learning to predict clinically significant patient improvement during a four-week interdisciplinary Intensive Outpatient Program (IOP) for traumatic brain injury (TBI) at the National Intrepid Center of Excellence (NICoE).
Assessment of brain injury characterization and outcomes were measured in 790 active duty service members at the NICoE, Walter Reed National Military Medical Center Bethesda Maryland. Demographic and self-reported measures of posttraumatic stress, depression, anxiety, post-concussion symptoms, and sleep were assessed upon admission. Total scores and symptom cluster scores for self-report measures were calculated. Clinically significant improvement from pre- to post NICoE IOP was operationally defined as clinically significant changes in posttraumatic stress and post-concussion symptoms. Two datasets were created: one including demographics and total scores on self-report measures and one including demographics, total scores, and symptom cluster scores for relevant self-report measures. Extreme gradient boosting (XGBoost) models were trained to predict group identification (clinically significant improvement vs. not significant improvement), where a binary logistic objective function is used to minimize the log loss between the predicted probabilities. Model performance and feature ranking were then evaluated on test datasets.
The performance and feature importance of two models to predict group identification were evaluated, where the model including only demographics and total self-report measures performed with an AUC of 75% with the accuracy of 68%, compared to the model incorporating demographics and symptom cluster measures improved the AUC to 79% with 72% accuracy. The top five features contributing to the model with symptom clusters included the posttraumatic stress arousal, avoidance, and reexperiencing sub-scores, education, and postconcussive symptoms cognitive sub-score.
Utilization of the XGBoost models demonstrated acceptable discrimination for determining key factors associated with clinically significant improvement for SMs following participation in an interdisciplinary IOP using demographics and self-report measures. Severity of posttraumatic stress symptoms upon admission was the greatest predictors of clinically significant improvement in this model of care. Incorporating ML algorithms into clinical care is a precision medicine approach that may accurately predict treatment efficacy leading to improved healthcare resource allocation and patient outcomes.
