Records |
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 |
Title |
Visualizing Early Warning Signs of Behavioral Crisis in Military Veterans: Empowering Peer Decision Support |
Type |
Conference Article |
Year |
2019 |
Publication |
Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management |
Abbreviated Journal |
Iscram 2019 |
Volume |
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Issue |
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Pages |
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Keywords |
crisis, mental health, visualization, veterans, clinical decision |
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. |
Address |
Marquette University;Medical College of Wisconsin;Dryhootch of America |
Corporate Author |
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Thesis |
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Publisher |
Iscram |
Place of Publication |
Valencia, Spain |
Editor |
Franco, Z.; González, J.J.; Canós, J.H. |
Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
|
ISSN |
2411-3387 |
ISBN |
978-84-09-10498-7 |
Medium |
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Track |
T11- Community Engagement & Healthcare Systems |
Expedition |
|
Conference |
16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019) |
Notes |
|
Approved |
no |
Call Number |
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Serial |
1948 |
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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 |
Title |
Detecting & Visualizing Crisis Events in Human Systems: an mHealth Approach with High Risk Veterans |
Type |
Conference Article |
Year |
2018 |
Publication |
ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2018 |
Volume |
|
Issue |
|
Pages |
874-885 |
Keywords |
Mental health crisis, computational psychology, wearable sensors, aggression, veterans |
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. |
Address |
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Corporate Author |
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Thesis |
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Publisher |
Rochester Institute of Technology |
Place of Publication |
Rochester, NY (USA) |
Editor |
Kees Boersma; Brian Tomaszeski |
Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
978-0-692-12760-5 |
Medium |
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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 |
Notes |
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Approved |
no |
Call Number |
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Serial |
2159 |
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