Application of Machine Learning in Prognosis and Trajectory of Post-Traumatic Stress Disorder in Children

Subjects: Psychology >> Psychological Measurement submitted time 2021-11-15

刘笑晗 陈明隆 郭静

Abstract： Abstract： Post-traumatic stress disorder (PTSD) has negative effects on children's development, even into adulthood. However, traditional diagnostic methods are difficult to quickly, objectively, and accurately identify and diagnose PTSD in children. Machine learning, as an emerging method to deal with a large number of variables and data, has gradually been applied to the research of early prediction, recognition, and auxiliary diagnosis of PTSD in children. Machine learning, with its advantages in performance and algorithm, can be applied to the recognition and prognosis of PTSD in children. Compared with self-reported diagnosis, the process of identifying and diagnosing PTSD in children through machine learning has unique advantages of high efficiency, objective accuracy, and resource-saving. Machine learning also has limitations in terms of hardware cost, algorithm selection, and prediction accuracy. In the future, researchers need to further improve the accuracy of machine learning diagnosis and recognition of PTSD in children and combine machine learning algorithms with traditional diagnosis methods for more exploration and application.

Peer Review Status:Awaiting Review

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