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Md Fitrat Hossain; Thomas Kissane; Priyanka Annapureddy; Wylie Frydrychowicz; Sheikh Iqbal Ahamed; Naveen Bansal; Praveen Madiraju; Niharika Jain; Mark Flower; Katinka Hooyer; Lisa Rein; Zeno Franco |
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Title |
Implementing Algorithmic Crisis Alerts in mHealth Systems for Veterans with PTSD |
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Conference Article |
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Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
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Pages |
122-133 |
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Keywords |
Crisis; Machine Learning Algorithms; mHealth; PTSD |
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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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Address |
Marquette University; Marquette University; Marquette University; Marquette University; Marquette University; Marquette University; Marquette University; Marquette University; Mental Health America; Medical College of Wisconsin; Medical College of Wisconsin; Medical College of Wisconsin |
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Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
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Language |
English |
Summary Language |
English |
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ISSN |
978-1-949373-27-12 |
ISBN |
2411-3398 |
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Track |
AI Systems for Crisis and Risks |
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Conference |
17th International Conference on Information Systems for Crisis Response and Management |
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Notes |
mdfitrat.hossain@marquette.edu |
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no |
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Call Number |
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Serial |
2213 |
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Author |
Zeno Franco; Katinka Hooyer; Tanvir Roushan; Casey O'Brien; Nadiyah Johnson; Bill Watson; Nancy Smith-Watson; Bryan Semaan; Mark Flower; Jim Tasse; Sheikh Iqbal Ahamed |
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Title |
Detecting & Visualizing Crisis Events in Human Systems: an mHealth Approach with High Risk Veterans |
Type |
Conference Article |
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Year |
2018 |
Publication |
ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2018 |
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Pages |
874-885 |
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Keywords |
Mental health crisis, computational psychology, wearable sensors, aggression, veterans |
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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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Publisher |
Rochester Institute of Technology |
Place of Publication |
Rochester, NY (USA) |
Editor |
Kees Boersma; Brian Tomaszeski |
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Language |
English |
Summary Language |
English |
Original Title |
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Abbreviated Series Title |
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Edition |
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ISSN |
2411-3387 |
ISBN |
978-0-692-12760-5 |
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Track |
Community Engagement & Healthcare Systems |
Expedition |
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Conference |
ISCRAM 2018 Conference Proceedings - 15th International Conference on Information Systems for Crisis Response and Management |
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Approved |
no |
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Call Number |
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Serial |
2159 |
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