Citation

Ge F, Li Y, Yuan M, Zhang J, Zhang W. J. Affect. Disord. 2019; ePub(ePub): ePub.

Affiliation

Mental Health Center of West China Hospital, Sichuan University, Chengdu 610041 Sichuan, P. R. China. Electronic address: weizhang27@163.com.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.jad.2019.11.079

PMID

31759663

Abstract

BACKGROUND: Evidence has identified risk factors associated with individuals with trauma exposure who develop posttraumatic stress disorder (PTSD). How to combine risk factors to predict probable PTSD in young survivors using machine learning is limited. The study aimed to integrated multiple measures at 2 weeks after the earthquake using machine learning for the prediction of probable PTSD at 3 months after earthquake.

METHODS: A total of 2099 young survivors with earthquake exposure were included. We integrated multiple domains of variables to 'train' a machine learning algorithm (XGBoost). Thirty-one combination types were implemented and evaluated. The resulting XGBoost was utilized in identifying individual participants as either probable PTSD or no PTSD.

RESULTS: Any combination type predicted young survivor probable PTSD, with prediction accuracies ranging between 66%-80% (p < 0.05). In particular, the combination of earthquake experience, everyday functioning, somatic symptoms and sleeping correctly predicted 683 out of 802 cases of probable PTSD, translating to a classical accuracy of 74.476% (85.156% sensitivity and 60.366% specificity) and an area under the curve of 0.80. The most relevant variables (e.g. age, sex, property loss and a sedentary lifestyle) revealed in the present study. LIMITATIONS: Participants from a specific district might limit the generalizability of our results. Self-report questionnaires and non-standardized measures were used to assess symptoms.

CONCLUSION: Detection of probable PTSD according to self-reported measurement data is feasible, may improve operational efficiencies via enabling targeted intervention, before manifestation of symptoms.

Copyright © 2019 Elsevier B.V. All rights reserved.

Language: en

Keywords

Early prediction; Longitudinal research; Machine learning; Probable posttraumatic stress disorder; Young