2017
DOI: 10.3390/ijerph14040361
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The Promise and the Challenge of Technology-Facilitated Methods for Assessing Behavioral and Cognitive Markers of Risk for Suicide among U.S. Army National Guard Personnel

Abstract: Suicide was the 10th leading cause of death for Americans in 2015 and rates have been steadily climbing over the last 25 years. Rates are particularly high amongst U.S. military personnel. Suicide prevention efforts in the military are significantly hampered by the lack of: (1) assessment tools for measuring baseline risk and (2) methods to detect periods of particularly heightened risk. Two specific barriers to assessing suicide risk in military personnel that call for innovation are: (1) the geographic dispe… Show more

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Cited by 8 publications
(3 citation statements)
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“…Finally, this test was based on computer slides and considers the importance of innovation present also in the area of psychological assessment. Future studies could verify the feasibility and relevance of using a mobile application version of this test, which would facilitate its application by professionals who do not have access to a computer at the time of the assessment [ 42 , 43 ].…”
Section: Discussionmentioning
confidence: 99%
“…Finally, this test was based on computer slides and considers the importance of innovation present also in the area of psychological assessment. Future studies could verify the feasibility and relevance of using a mobile application version of this test, which would facilitate its application by professionals who do not have access to a computer at the time of the assessment [ 42 , 43 ].…”
Section: Discussionmentioning
confidence: 99%
“…These challenges hint at the potential benefits of involving automatic behavior annotation, in which data-driven machine learning techniques are employed to automatically extract behavioral information directly from data, rather than relying on time-consuming and expensive annotations from human experts. Such behavior analysis work has been shown to be effective at identifying behaviors during interactions in domains such as couple therapy [7,8,9], depression [10,11,12] and suicide risk assessment [13,14,15,16]. However, due to potential domain mismatch, obtaining accurate performance in one domain by utilizing well-trained behavior analysis systems from a different domain is not straightforward.…”
Section: Introductionmentioning
confidence: 99%
“…While by now the complete description of person's state with machine learning algorithms is beyond reach, these algorithm are successfully implemented for some specific objectives. For instance, to predict bonding in conversation (Jaques, McDuff, Kim, & Picard, 2016), to make individual prediction about well-being and mood (Taylor, Jaques, Nosakhare, Sano, & Picard, 2017) or to estimate soldier's suicide risk (Baucom et al 2017).…”
Section: Introductionmentioning
confidence: 99%