A Calculated Risk: Systematic Review and Metaanalysis of the Conditional Risk for Posttraumatic Stress Disorder (PTSD) in the Brazilian General Population

Luz, Mariana da; Berger, William; Figueira, Ivan; Coutinho, Eridan M.; Mendlowicz, Mauro V.

doi:10.1016/s0924-9338(15)31194-9

Cited by 1 publication

(2 citation statements)

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“…This study evaluated the implementation of an artificial neural network algorithm to forecast PTSD using ecological risk factors for PTSD that can be easily collected. The ecological factors have been selected through an exhaustive literature review that identified many ecological factors that researchers (Bentley et al, 2014;Bowen et al, 2017;Gelkopf et al, 2012;Itzhaky et al, 2018;Luz et al, 2015;McKeever & Huff, 2003;Rosenbaum et al, 2015;Stanley et al, 2018;Stein et al, 2013;Vaughn-Coaxum et al, 2015;Xue et al, 2015) found to influence PTSD risk.…”

Section: Discussionmentioning

confidence: 99%

“…An exhaustive literature review was conducted with the aim of identifying ecological risk factors for PTSD. The main categories of ecological risk factors that were identified were socioeconomic status (Luz, Berger, Figueira, Coutinho, & Mendlowicz, 2015), age, education, gender (Xue et al, 2015), religiosity (Bentley, Ahmad, & Thoburn, 2014), social support (Stanley et al, 2018), economic loss following the traumatic event (Gelkopf et al, 2012), physical activity (Rosenbaum et al, 2015), substance abuse (Bowen, De Boer, & Bergman, 2017) ,family stressors (Vaughn-Coaxum, Smith, Iverson, & Vogt, 2015), sense of belonging (Itzhaky, Weiss-Dagan, & Taubman-Ben-Ari, 2018), level of exposure (Xue et al, 2015), intensity of trauma, and history of trauma (McKeever & Huff, 2003;Stein et al, 2013;Xue et al, 2015). A short questionnaire of 37 questions measuring these factors was designed for the purpose of this study.…”

Section: Methodsmentioning

confidence: 99%

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Quantitative Forecasting of Risk for PTSD Using Ecological Factors: A Deep Learning Application

Mor

Dardeck

2018

JSBHS

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Forecasting the risk for mental disorders from early ecological information holds benefits for the individual and society. Computational models used in psychological research, however, are barriers to making such predictions at the individual level. Preexposure identification of future soldiers at risk for posttraumatic stress disorder (PTSD) and other individuals, such as humanitarian aid workers and journalists intending to be potentially exposed to traumatic events, is important for guiding decisions about exposure. The purpose of the present study was to evaluate a machine learning approach to identify individuals at risk for PTSD using readily collected ecological risk factors, which makes scanning a large population possible. An exhaustive literature review was conducted to identify multiple ecological risk factors for PTSD. A questionnaire assessing these factors was designed and distributed among residents of southern Israel who have been exposed to terror attacks; data were collected from 1,290 residents. A neural network classification algorithm was used to predict the likelihood of a PTSD diagnosis. Assessed by cross-validation, the prediction of PTSD diagnostic status yielded a mean area under receiver operating characteristics curve of .91 (<em>F </em>score = .83). This study is a novel attempt to implement a neural network classification algorithm using ecological risk factors to predict potential risk for PTSD. Preexposure identification of future soldiers and other individuals at risk for PTSD from a large population of candidates is feasible using machine learning methods and readily collected ecological factors.

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