机器学习在儿童创伤后应激障碍识别及转归预测中的应用

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

创伤后应激障碍(PTSD)会给儿童发展带来负面效应,其影响甚至延续至成年期。然而传统诊断方式难以做到快速、客观、准确的识别和诊断儿童PTSD,机器学习作为一种处理大量变量和数据的新兴方法,逐渐被应用到儿童PTSD的早期预测、识别及辅助诊断等研究中。机器学习凭借其性能、原理等方面的优势,可被应用在儿童PTSD的识别与转归领域。相比自我报告式的诊断,通过机器学习辅助识别和诊断儿童PTSD的过程具有效率高、客观准确、节约资源等独特优势。然而,机器学习也在硬件成本、算法选择和预测准确度等方面存在局限性。未来研究人员需要进一步提高机器学习诊断识别儿童PTSD的准确率,并将机器学习算法同传统诊断方法结合进行更多的探索和应用。

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.