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Md Fitrat Hossain, Thomas Kissane, Priyanka Annapureddy, Wylie Frydrychowicz, Sheikh Iqbal Ahamed, Naveen Bansal, et al. (2020). Implementing Algorithmic Crisis Alerts in mHealth Systems for Veterans with PTSD. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 122–133). Blacksburg, VA (USA): Virginia Tech.
Abstract: This paper seeks to establish a machine learning driven method by which a military veteran with Post-Traumatic Stress Disorder (PTSD) is classified as being in a crisis situation or not, based upon a given set of criteria. Optimizing alerting decision rules is critical to ensure that veterans at highest risk for mental health crisis rapidly receive additional attention. Subject matter experts in our team (a psychologist, a medical anthropologist, and an expert veteran), defined acute crisis, early warning signs and long-term crisis from this dataset. First, we used a decision tree to find an early time point when the peer mentors (who are also veterans) need to observe the behavior of veterans to make a decision about conducting an intervention. Three different machine learning algorithms were used to predict long term crisis using acute crisis and early warning signs within the determined time point.
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Olawunmi George, Rizwana Rizia, MD Fitrat Hossain, Nadiyah Johnson, Carla Echeveste, Jose Lizarraga Mazaba, et al. (2019). Visualizing Early Warning Signs of Behavioral Crisis in Military Veterans: Empowering Peer Decision Support. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: Several attempts have been made at creating mobile solutions for patients with mental disorders. A preemptive approach would definitely outdo a reactive one. This project seeks to ensure better crisis detection, by assigning patients (veterans) to caregivers (mentors). This is called the mentor-mentee approach. Enhanced with the use of mobile technology, veterans can stay connected in their daily lives to mentors, who have gone through the same traumatic experiences and have overcome them. A mobile application for communication between veterans and their mentors has been developed, which helps mentors get constant feedback from their mentees about their state of well-being. However, being able to make good deductions from the data given as feedback is of great importance. Under-represent ing or over-representing the data could be dangerously misleading. This paper presents the design process in this project and the key things to note when designing a data visualization for
timely crisis detection and decision-making.
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Zeno Franco, Katinka Hooyer, Tanvir Roushan, Casey O'Brien, Nadiyah Johnson, Bill Watson, et al. (2018). Detecting & Visualizing Crisis Events in Human Systems: an mHealth Approach with High Risk Veterans. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 874–885). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Designing mHealth applications for mental health interventions has largely focused on education and patient self-management. Next generation applications must take on more complex tasks, including sensor-based detection of crisis events, search for individualized early warning signs, and support for crisis intervention. This project examines approaches to integrating multiple worn sensors to detect mental health crisis events in US military veterans. Our work has highlighted several practical and theoretical problems with applying technology to evaluation crises in human system, which are often subtle and difficult to detect, as compared to technological or natural crisis events. Humans often do not recognize when they are in crisis and under-report crises to prevent reputational damage. The current project explores preliminary use of the E4 Empatica wristband to characterize acute aggression using a combination of veteran self-report data on anger, professional actors simulating aggressive events, and preliminary efforts to discriminate between crisis data and early warning sign data.
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