A neural network based model for predicting psychological conditions

Abstract

Preventive care attempts to inform individuals and clinicians of potential complications or conditions a patient might encounter. With the recent interest on leveraging big data in the healthcare domain to better design data-driven models for preventive medicine and the increased awareness of the long-lasting effects of concussions, being able to predict psychological conditions post concussion can have a paramount effect on mild traumatic brain injury patients. We present a neural network model that is able to predict the likelihood of developing psychological conditions such as anxiety, behavioral disorders, depression, and posttraumatic stress disorder. We analyzed the effectiveness of our model against a dataset of 89,840 patients. Our results show that we are able to achieve accuracies ranging from 73% to 95% for each of the clinical conditions under consideration, with an overall accuracy of 82.35% for all conditions.