Post-Traumatic Stress Disorder (PTSD) is one of the most common mental health disorders prevalent in the US. Most alarming, PTSD occurs at double the rate for combat veterans compared to the general population. Severity of PTSD is associated with risk taking behaviors such as substance abuse, non-suicidal self-injury, sexual risk behaviors, among other negative behaviors. Psychological disorders are often preceded by crisis events, thus monitoring for crisis events can help prevent risky behavior in veterans. Ecological momentary assessment techniques are effective in capturing possible crisis events for veterans. Mobile apps are commonly used to gather such behavioral changes in participants. Crisis events collected from m-health can be analyzed for the identification of long- term PTSD risk. Early identification of risk can help in planning intervention to mitigate the risk. Many scholars have used traditional statistical and machine learning methods for the prediction of mental health issues in individuals. But these models lack transparency in how decisions are made. Providing justifications for the predictions can increase the reliability of the model. Our research focused on developing an explainable prediction model using class association rules to identify veterans at risk of persistent PTSD. The generated association rules serve as precursors to the long-term crisis in veterans. Results of the analysis showed that having no family support, little or no interest in hobbies, stress and lack of sleep are some of the influencing factors of persistent PTSD in veterans.

In response to the COVID-19 pandemic, the White House and a coalition of leading research groups have prepared the COVID-19 Open Research Dataset (CORD-19) containing over 51,000 scholarly articles, including over 40,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses. Medical professional including physicians frequently seek answers to specific questions to improve guidelines and decisions. The huge resource of medical literature is important sources to generate new insights that can help medical communities to provide relevant knowledge and overall fight against the infectious disease. There are ongoing attempts to develop intelligent systems to automatically extract relevant knowledge from many unstructured documents. In this paper, we propose an efficient question answering framework based on automatically analyzing thousands of articles to generate both long text answers (sections/ paragraphs) in response to the questions that are posed by medical communities. In the process of developing the framework, we explored natural language processing techniques like query expansion, data preprocessing, and vector space models early. We show the initial results of an example query answering for the incubation period.