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

Abstract

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.

Analyzing Perceived Psychological and Social Stress of University Students: A Machine Learning Approach

“…Both grid search and evolutionary GA demonstrated acceptable levels of accuracy, and most algorithms perform better using evolutionary GA like previous study of stress detection. The evolutionary GA performs better in most of the algorithms in experiment B and C. In most cases, evolutionary GA outperforms GSCV; however, GSCV significantly boosts accuracy [ 35 ]. In all three experiments, evolutionary GA surpasses the other approaches in most cases.…”

Digital Biomarkers in Living Labs for Vulnerable and Susceptible Individuals: An Integrative Literature Review

“…In most studies, measurements such as ECG, 34 37 pulse rate, 39 and acceleration 32 35 36 were collected as digital biomarkers since the subjects’ body data or location data were required in their daily lives. In particular, watches 34 36 and waist- or writstbands 32 were widely used, as were voice recognition, 37 smartphones, 38 40 41 and portable sensors. 39 The biomarker collection methods varied according to the type of biomarker and type of disability.…”

“…Since wearable sensors could cause discomfort to patients during sleep, recording equipment attached to the ceiling was used for patients with sleep disordered breathing. 37 For those with post-traumatic stress disorder, 40 there may be physical “avoidance,” especially in patients with highly heterogeneous disorders.…”

“…Ma, et al 39 diagnosed obstructive sleep apnea syndrome by examining the occurrence of apnea. Lekkas and Jacobson 40 predicted PTSD diagnostic status using global positioning system-based location data. Wintgens, et al 33 tested fecal calprotectin level using stool samples.…”

Abnormal Cytokines in Trauma Patients Explained by Obesity, Musculoskeletal Disease, Smoking, and Lung Disease

“…Large longitudinal or randomized studies are difficult or impossible to address these research questions. Recent introductions of artificial intelligence show promising results in untangling the causes and effects of trauma and PTSD [101].…”