PTSD has been a significant problem in our society, and much research is going on to predict and diagnose PTSD. Our method helps to predict PTSD in its early stage with the help of physiological markers like heart rate and breath rate, which, combined with prior information about the patient like PTSD history, exposure to trauma, substance abuse disorder, and other information, helps to create a risk score with more accuracy. Due to the lack of a public dataset on this domain, we used different univariate relationships of physiological markers with PTSD to create a multimodal model using a slight modification of the naïve Bayes algorithm. Implementation of a microcontroller which will be used by the user and worn on the wrist along with the cloud IoT platform, and a mobile app is created to demo the possibility of the system which helps healthcare providers and users to timely track and monitor PTSD risks with background information and priors accurately.

Recommended Citation: KC B., Rachakonda L., Modaresnezhad M., Guinn C., (2023). PTSD Detection Using Physiological Markers. UNCW MS CSIS Proceedings. V. 17 , N. 5 .