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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 |
Original Title |
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Edition |
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ISSN |
978-1-949373-27-12 |
ISBN |
2411-3398 |
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Track |
AI Systems for Crisis and Risks |
Expedition |
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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 |
Olawunmi George; Rizwana Rizia; MD Fitrat Hossain; Nadiyah Johnson; Carla Echeveste; Jose Lizarraga Mazaba; Katinka Hooyer; Zeno Franco; Mark Flower; Praveen Madiraju; Lisa Rein |
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Title |
Visualizing Early Warning Signs of Behavioral Crisis in Military Veterans: Empowering Peer Decision Support |
Type |
Conference Article |
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Year |
2019 |
Publication |
Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management |
Abbreviated Journal |
Iscram 2019 |
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Pages |
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Keywords |
crisis, mental health, visualization, veterans, clinical decision |
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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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Address |
Marquette University;Medical College of Wisconsin;Dryhootch of America |
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Publisher |
Iscram |
Place of Publication |
Valencia, Spain |
Editor |
Franco, Z.; González, J.J.; Canós, J.H. |
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Language |
English |
Summary Language |
English |
Original Title |
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Edition |
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ISSN |
2411-3387 |
ISBN |
978-84-09-10498-7 |
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Track |
T11- Community Engagement & Healthcare Systems |
Expedition |
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Conference |
16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019) |
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Notes |
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Approved |
no |
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Serial |
1948 |
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