Recent research has identified a number of pre-traumatic, peri-traumatic and post-traumatic psychological and ecological factors that put an individual at increased risk for developing PTSD following a life-threatening event. While these factors have been found to be associated with PTSD in univariate analyses, the complex interactions of these risk factors and how they contribute to individual trajectories of the illness are not yet well understood. In this study, we examine the impact of prior trauma, psychopathology, sociodemographic characteristics, community and environmental information, on PTSD onset in a nationally representative sample of adults in the United States, using machine learning methods to establish the relative contributions of each variable. Individual risk factors identified in Waves 1 of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) were combined with community-level data for the years concurrent to the NESARC Wave 1 (n = 43,093) and 2 (n = 34,653) surveys. Machine learning feature selection and classification analyses were used at the national level to create models using individual- and community-level variables that would best predict the new onset of PTSD at Wave 2. Our classification algorithms yielded 89.7 to 95.6% accuracy for predicting new onset of PTSD at Wave 2. A prior diagnosis of DSM-IV-TR Borderline Personality Disorder, Major Depressive Disorder or Anxiety Disorder conferred the greatest relative influence in new diagnosis of PTSD. Distal risk factors such as prior psychiatric diagnosis accounted for significantly greater relative risk than proximal factors (such as adverse event exposure). Our findings show that a machine learning classification approach can successfully integrate large numbers of known risk factors for PTSD into stronger models that account for high-dimensional interactions and collinearity between variables. We discuss the implications of these findings as pertaining to the targeted mobilization emergency mental health resources. These findings also inform the creation of a more comprehensive risk assessment profile to the likelihood of developing PTSD following an extremely adverse event.

中文翻译：

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使用机器学习对全国代表性样本中的 PTSD 诊断进行前瞻性预测


最近的研究发现了一些创伤前、创伤前后和创伤后的心理和生态因素，这些因素使个体在发生危及生命的事件后患上创伤后应激障碍（PTSD）的风险增加。虽然单变量分析发现这些因素与创伤后应激障碍有关，但这些风险因素之间复杂的相互作用以及它们如何影响个体的疾病轨迹尚不清楚。在这项研究中，我们使用机器学习方法来确定每个变量的相对贡献，以美国具有全国代表性的成年人样本为对象，研究既往创伤、精神病理学、社会人口特征、社区和环境信息对 PTSD 发病的影响。将国家酒精及相关病症流行病学调查 (NESARC) 第 1 波中确定的个人风险因素与 NESARC 第 1 波（n = 43,093）和第 2 波（n = 34,653）调查同期的社区级数据相结合。在国家层面使用机器学习特征选择和分类分析，使用个人和社区层面的变量创建模型，最好地预测第 2 波 PTSD 的新发作。我们的分类算法预测新发作的准确度为 89.7% 至 95.6%第 2 波的 PTSD。DSM-IV-TR 边缘性人格障碍、重度抑郁症或焦虑症的先前诊断对 PTSD 的新诊断具有最大的相对影响。远端风险因素（例如先前的精神病学诊断）所造成的相对风险显着高于近端因素（例如不良事件暴露）。 我们的研究结果表明，机器学习分类方法可以成功地将大量已知的 PTSD 风险因素整合到更强大的模型中，以解释变量之间的高维相互作用和共线性。我们讨论这些发现对于有针对性地调动紧急心理卫生资源的影响。这些发现还为创建更全面的风险评估档案提供了依据，以评估在极端不良事件后发生创伤后应激障碍（PTSD）的可能性。