Psychology

Machine learning to reveal hidden risk combinations for the trajectory of posttraumatic stress disorder symptoms

Y. Takahashi, K. Yoshizoe, et al.

This groundbreaking research uncovers 56 significant combinational risk factors affecting the long-term trajectory of PTSD symptoms in 624 individuals impacted by the Great East Japan Earthquake. Conducted by Yuta Takahashi and colleagues, the study emphasizes the relevance of comprehensive analysis for multifactorial psychiatric conditions.

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Abstract

This study investigated the combinational risk factors influencing the long-term trajectory of posttraumatic stress disorder (PTSD) symptoms in 624 individuals severely affected by the Great East Japan Earthquake. Using the Massive Parallel Limitless-Arity Multiple-testing Procedure (MP-LAMP), the study identified 56 significant combinational risk factors, including 15 independent variables, which were not detected by conventional bivariate analysis. The strongest association was found with the combination of short resting time, short walking time, unemployment, and evacuation without preparation. The results highlight the importance of comprehensive combinational analysis for understanding multifactorial psychiatric symptoms.

Publisher

Scientific Reports

Published On

Dec 10, 2020

Authors

Yuta Takahashi, Kazuki Yoshizoe, Masao Ueki, Gen Tamiya, Yu Zhiqian, Yusuke Utsumi, Atsushi Sakuma, Koji Tsuda, Atsushi Hozawa, Ichiro Tsuji, Hiroaki Tomita

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