Towards Predicting Risky Behavior Among Veterans with PTSD by Analyzing Gesture Patterns

Roushan, Tanvir; Adib, Riddhiman; Johnson, Nadiyah; George, Olawunmi; Hossain, Fitrat; Franco, Zeno; Hooyer, Katinka; Ahamed, Sheikh Iqbal

doi:10.1109/compsac.2019.00104

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…In the aim of helping clinician and mental healthcare providers who have limited time to evaluate the questionnaires, the researchers utilize an information gain driven ranking procedure to identify the most informative questions. Similarly, researchers in (T. Roushan et al, 2019) developed a predictive model that can identify the gestures resulting in the prediction of risky behaviors among the veterans who suffer from PTSD.…”

Section: Introductionmentioning

confidence: 99%

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Travma Sonrası Stres Bozukluğu Yönetimi: Makine Öğrenmesi Yaklaşımı

Dağtekin¹,

Seven²,

Balsever³

et al. 2020

European Journal of Science and Technology

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Post-traumatic stress disorder (PTSD) is defined as a traumatic injury developed after facing or witnessing a life-threatening experience or event such as a natural disaster, a pandemic, a serious accident, a terrorist act, war/combat, rape or other violent personal assault. Machine Learning (ML) has been widening its scope on psychological and physical healthcare for a decade by predicting detecting, personalizing, digitalizing, preventing risks, monitoring, and classifying PTSD based clinical mental diseases. In this study, we predict PTSD scores of the participants obtained from Mississippi-Civilian Version Scale and DSM-5 (PCL-5) Scale by applying ML. For our experiments we used the following methods namely k-nearest neighbor (k-nn), support vector machine (SVM), decision tree (DT), Gaussian Naive Bayes (GNB) and artificial neural networks (ANN). According to the comparison of the prediction results Considering PTSD prediction classification performance results for Mississippi (Civilian version) scale data set in comparison to the above mentioned methods, ANN offers the best prediction in terms of accuracy, F1 score and recall. However, Gaussian Naive Bayes (GNB) gives the best prediction score in terms of precision. On the other hand, when we applied all these methods to DSM-5 (PCL-5) scale data set, we have observed that ANN offers the best prediction in terms of accuracies, F1 score and precision. Nevertheless, in terms of recall Gaussian Naive Bayes (GNB) gives the best prediction score. By applying all the methods to these two different data sets and comparing the results, we demonstrate which method can be more efficient in prediction, diagnosis and monitoring the patients with PTSD.

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Section: Introductionmentioning

confidence: 99%

“…N. Karancı et al, 2009; J. L. Gradus et al, 2017;J. L. Steel et al, 2011;T. Roushan et al, 2019) and suffer from nightmares, flashbacks and disturbing thoughts (T. Armstrong et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Travma Sonrası Stres Bozukluğu Yönetimi: Makine Öğrenmesi Yaklaşımı

Dağtekin¹,

Seven²,

Balsever³

et al. 2020

European Journal of Science and Technology

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Using artificial intelligence and longitudinal location data to differentiate persons who develop posttraumatic stress disorder following childhood trauma

Lekkas

Jacobson

2021

Sci Rep

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Post-traumatic stress disorder (PTSD) is characterized by complex, heterogeneous symptomology, thus detection outside traditional clinical contexts is difficult. Fortunately, advances in mobile technology, passive sensing, and analytics offer promising avenues for research and development. The present study examined the ability to utilize Global Positioning System (GPS) data, derived passively from a smartphone across seven days, to detect PTSD diagnostic status among a cohort (N = 185) of high-risk, previously traumatized women. Using daily time spent away and maximum distance traveled from home as a basis for model feature engineering, the results suggested that diagnostic group status can be predicted out-of-fold with high performance (AUC = 0.816, balanced sensitivity = 0.743, balanced specificity = 0.8, balanced accuracy = 0.771). Results further implicate the potential utility of GPS information as a digital biomarker of the PTSD behavioral repertoire. Future PTSD research will benefit from application of GPS data within larger, more diverse populations.

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Towards Predicting Risky Behavior Among Veterans with PTSD by Analyzing Gesture Patterns

Cited by 2 publications

References 16 publications

Travma Sonrası Stres Bozukluğu Yönetimi: Makine Öğrenmesi Yaklaşımı

Travma Sonrası Stres Bozukluğu Yönetimi: Makine Öğrenmesi Yaklaşımı

Using artificial intelligence and longitudinal location data to differentiate persons who develop posttraumatic stress disorder following childhood trauma

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