Leveraging Big Data to Model the Likelihood of Developing Psychological Conditions After a Concussion

A concussion is an invisible and poorly understood mild traumatic brain injury (mTBI) that can alter the way the brain functions. Patients who have screened positive for mTBI are at an increased risk of depression, post-traumatic stress disorder (PTSD), headaches, sleep disorders, and other neurological and psychological problems. Early detection of psychological conditions such as PTSD following a concussion might improve the overall outcome of a patient and could potentially reduce the cost associated with intense interventions often required when conditions go untreated for a long time. Statistical and predictive models that leverage large-scale clinical repositories and use pre-existing conditions to determine the probability of a patient developing psychological conditions following a concussion have not been widely studied. This paper presents an SVM-based model that has been trained with a longitudinal dataset of over 5.3 million clinical encounters of 89,840 service members that have sustained a concussion. The model has been tested and validated with over 16,045 patients that developed PTSD and it has shown an accuracy of over 85% (AUC of 86.52%) at predicting the condition within the first year following the injury.