系统回顾机器学习在创伤后应激障碍中的发现及其与理论模型的关系

Wivine Blekic, Fabien D’Hondt, Arieh Y. Shalev, Katharina Schultebraucks
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摘要

近年来,机器学习(ML)技术在创伤后应激障碍(PTSD)预测研究中的应用越来越多。然而,对临床相关性和ML发现的普遍性的担忧阻碍了临床医生和研究人员的实施。在这篇系统综述中,我们检查了(1)使用ML方法确定的创伤前、创伤周围和创伤后风险因素与对该障碍的理论认识相吻合的程度;(2)是否通过ML技术获得了新的见解;(3) ML研究结果与以往的研究相结合,是否能够建立一个包括预测因子类别及其理论相关性的创伤后应激障碍风险综合模型。我们回顾了PubMed、Web of Science和Scopus中关于PTSD危险因素的ML研究。如果研究明确了何时收集预测因子和PTSD症状与创伤性事件的时间关系,则纳入研究。共纳入30项研究,12908名参与者(平均年龄36.5岁)。在从所有研究中提取出15个最重要的预测因子后,我们将它们分为创伤前、创伤中和创伤后暴露预测因子,并检查它们与已建立的创伤后应激障碍理论模型的关联。许多研究显示存在偏倚风险,使用预测模型偏倚风险评估工具(PROBAST)进行评估。然而,我们发现不同研究中确定的预测因素存在重叠,数据驱动的结果和理论驱动的研究之间存在一致性,以及通过ML确定的未被探索的预测因素。我们提出了一个整合数据驱动和理论驱动结果的PTSD风险综合模型,并讨论了未来的发展方向。我们强调关于如何应用和报告ML方法用于心理健康的标准的重要性。本系统综述综合了来自30项研究的证据,这些研究使用机器学习方法来识别创伤后应激障碍风险的预测因素。作者发现了研究中未充分开发的预测因子和预测因子的重叠,并发现了数据驱动结果和基于理论的模型之间的一致性。
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A systematic review of machine learning findings in PTSD and their relationships with theoretical models
In recent years, the application of machine learning (ML) techniques in research on the prediction of post-traumatic stress disorder (PTSD) has increased. However, concerns regarding the clinical relevance and generalizability of ML findings hamper their implementation by clinicians and researchers. Here in this systematic review we examined (1) the extent to which pre-, peri- and post-traumatic risk factors identified using ML approaches coincide with the theoretical understanding of the disorder; (2) whether new insights were gained through ML techniques; and (3) whether ML findings, combined with previous research, enable an integrative model of PTSD risk encompassing both predictor categories and their theoretical relevance. We reviewed ML studies on PTSD risk factors in PubMed, Web of Science and Scopus. Studies were included if they specified when predictors and PTSD symptoms were collected in temporal relation to the traumatic event. A total of 30 studies with 12,908 participants (mean age 36.5 years) were included. After extracting the 15 most important predictors from all studies, we categorized them into pre-, peri- and post-trauma exposure predictors and examined their associations with established theoretical models of PTSD. Many studies exhibited a risk of bias, assessed using the prediction model risk of bias assessment tool (PROBAST). However, we found overlaps in identified predictors across studies, a concordance between data-driven results and theory-driven research, and underexplored predictors identified through ML. We propose an integrative model of PTSD risk that incorporates both data-driven and theory-driven findings and discuss future directions. We emphasize the importance of standards on how to apply and report ML approaches for mental health. This systematic review synthesizes evidence from 30 studies using machine learning approaches to identify predictors for post-traumatic stress disorder risk. The authors detect underexplored predictors and overlaps in predictors across studies and find an alignment between data-driven results and theory-based models.
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