Establishing the diagnosis of trauma-related disorders such as Acute Stress Disorder (ASD) and Posttraumatic Stress Disorder (PTSD) have always been a challenge in clinical practice and in academic research, due to clinical and biological heterogeneity. Machine learning (ML) techniques can be applied to improve classification of disorders, to predict outcomes or to determine person-specific treatment selection. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with ASD or PTSD. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to May 2019. We found 806 abstracts and included 49 studies in our review. Most of the included studies used multiple levels of biological data to predict risk factors or to identify early symptoms related to PTSD. Other studies used ML classification techniques to distinguish individuals with ASD or PTSD from other psychiatric disorder or from trauma-exposed and healthy controls. We also found studies that attempted to define outcome profiles using clustering techniques and studies that assessed the relationship among symptoms using network analysis. Finally, we proposed a quality assessment in this review, evaluating methodological and technical features on machine learning studies. We concluded that etiologic and clinical heterogeneity of ASD/PTSD patients is suitable to machine learning techniques and a major challenge for the future is to use it in clinical practice for the benefit of patients in an individual level.

中文翻译：

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机器学习技术在创伤相关疾病中的应用：系统综述。

由于临床和生物学的异质性，建立与创伤有关的疾病的诊断，例如急性应激障碍（ASD）和创伤后应激障碍（PTSD）一直是临床实践和学术研究中的挑战。机器学习（ML）技术可用于改善疾病分类，预测结果或确定针对特定人群的治疗选择。我们旨在回顾有关使用机器学习技术评估ASD或PTSD受试者的现有文献。我们系统地搜索了PubMed，Embase和Web of Science直至2019年5月的任何语言的文章。我们找到806个摘要，包括49项研究。纳入的大多数研究都使用多种水平的生物学数据来预测危险因素或识别与PTSD相关的早期症状。其他研究使用ML分类技术将ASD或PTSD的个体与其他精神病患者或暴露于外伤的健康对照区分开。我们还发现了尝试使用聚类技术定义结果概况的研究，以及使用网络分析评估症状之间关系的研究。最后，我们在本次审查中提出了质量评估，评估了机器学习研究的方法和技术功能。