Application of machine learning in prognosis and trajectory of post-traumatic stress disorder in children

Abstract

Post-traumatic stress disorder (PTSD) could have negative effects on the development of children, and its impact can even last into adulthood. However, the 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. However, machine learning 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 children's PTSD recognition, and explore more combinations of machine learning and traditional diagnosis methods.