Wu Yuqi, Mao Kaining, Dennett Liz, Zhang Yanbo, Chen Jie
Electrical & Computer Engineering Department, Faculty of Engineering, University of Alberta, Edmonton, AB, Canada.
Scott Health Sciences Library, University of Alberta, Edmonton, AB, Canada.
Npj Ment Health Res. 2023 Sep 27;2(1):16. doi: 10.1038/s44184-023-00035-w.
Post-traumatic stress disorder (PTSD) is frequently underdiagnosed due to its clinical and biological heterogeneity. Worldwide, many people face barriers to accessing accurate and timely diagnoses. Machine learning (ML) techniques have been utilized for early assessments and outcome prediction to address these challenges. This paper aims to conduct a systematic review to investigate if ML is a promising approach for PTSD diagnosis. In this review, statistical methods were employed to synthesize the outcomes of the included research and provide guidance on critical considerations for ML task implementation. These included (a) selection of the most appropriate ML model for the available dataset, (b) identification of optimal ML features based on the chosen diagnostic method, (c) determination of appropriate sample size based on the distribution of the data, and (d) implementation of suitable validation tools to assess the performance of the selected ML models. We screened 3186 studies and included 41 articles based on eligibility criteria in the final synthesis. Here we report that the analysis of the included studies highlights the potential of artificial intelligence (AI) in PTSD diagnosis. However, implementing AI-based diagnostic systems in real clinical settings requires addressing several limitations, including appropriate regulation, ethical considerations, and protection of patient privacy.
创伤后应激障碍（PTSD）因其临床和生物学异质性，常常未得到充分诊断。在全球范围内，许多人在获得准确及时的诊断方面面临障碍。机器学习（ML）技术已被用于早期评估和结果预测，以应对这些挑战。本文旨在进行一项系统综述，以调查ML是否是一种有前景的PTSD诊断方法。在本综述中，采用统计方法综合纳入研究的结果，并为ML任务实施的关键考虑因素提供指导。这些因素包括：（a）为可用数据集选择最合适的ML模型；（b）根据所选诊断方法确定最佳ML特征；（c）根据数据分布确定合适的样本量；（d）实施合适的验证工具以评估所选ML模型的性能。我们筛选了3186项研究，并根据纳入标准在最终综述中纳入了41篇文章。在此我们报告，对纳入研究的分析突出了人工智能（AI）在PTSD诊断中的潜力。然而，在实际临床环境中实施基于AI的诊断系统需要解决若干限制，包括适当的监管、伦理考量以及患者隐私保护。
