Wang Jing, Ouyang Hui, Jiao Runda, Cheng Suhui, Zhang Haiyan, Shang Zhilei, Jia Yanpu, Yan Wenjie, Wu Lili, Liu Weizhi
Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, 200433, Shanghai, China.
The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, 200433, Shanghai, China.
NPJ Digit Med. 2024 May 9;7(1):121. doi: 10.1038/s41746-024-01117-5.
Posttraumatic stress disorder (PTSD) recently becomes one of the most important mental health concerns. However, no previous study has comprehensively reviewed the application of big data and machine learning (ML) techniques in PTSD. We found 873 studies meet the inclusion criteria and a total of 31 of those in a sample of 210,001 were included in quantitative analysis. ML algorithms were able to discriminate PTSD with an overall accuracy of 0.89. Pooled estimates of classification accuracy from multi-dimensional data (0.96) are higher than single data types (0.86 to 0.90). ML techniques can effectively classify PTSD and models using multi-dimensional data perform better than those using single data types. While selecting optimal combinations of data types and ML algorithms to be clinically applied at the individual level still remains a big challenge, these findings provide insights into the classification, identification, diagnosis and treatment of PTSD.
创伤后应激障碍（PTSD）最近成为最重要的心理健康问题之一。然而，以前没有研究全面综述过大数据和机器学习（ML）技术在PTSD中的应用。我们发现873项研究符合纳入标准，在210,001个样本中共有31项被纳入定量分析。ML算法能够以0.89的总体准确率区分PTSD。来自多维度数据的分类准确率合并估计值（0.96）高于单一数据类型（0.86至0.90）。ML技术可以有效分类PTSD，使用多维度数据的模型比使用单一数据类型的模型表现更好。虽然选择数据类型和ML算法的最佳组合以在个体水平上进行临床应用仍然是一个巨大挑战，但这些发现为PTSD的分类、识别、诊断和治疗提供了见解。
