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，此前还没有研究全面回顾大数据和机器学习（ML）技术在创伤后应激障碍（PTSD）中的应用。我们发现 873 项研究符合纳入标准，210,001 份样本中总共有 31 项纳入定量分析。 ML 算法能够以 0.89 的总体准确度区分 PTSD。多维数据分类准确度的汇总估计 (0.96) 高于单一数据类型（0.86 至 0.90）。机器学习技术可以有效地对 PTSD 进行分类，并且使用多维数据的模型比使用单一数据类型的模型表现更好。虽然选择在个体层面临床应用的数据类型和机器学习算法的最佳组合仍然是一个巨大的挑战，但这些发现为 PTSD 的分类、识别、诊断和治疗提供了见解。