Department of Clinical Psychology, Norwich Medical School, University of East Anglia, UK.
Dementia Research, Norwich Medical School, University of East Anglia, UK.
J Anxiety Disord. 2022 Dec;92:102642. doi: 10.1016/j.janxdis.2022.102642. Epub 2022 Oct 27.
Youth receiving medical care for injury are at risk of PTSD. Therefore, accurate prediction of chronic PTSD at an early stage is needed. Machine learning (ML) offers a promising approach to precise prediction and interpretation.
The study proposes a clinically useful predictive model for PTSD 6-12 months after injury, analyzing the relationship among predictors, and between predictors and outcomes.
A ML approach was utilized to train models based on 1167 children and adolescents of nine perspective studies. Demographics, trauma characteristics and acute traumatic stress (ASD) symptoms were used as initial predictors. PTSD diagnosis at six months was derived using DSM-IV PTSD diagnostic criteria. Models were validated on external datasets. Shapley value and partial dependency plot (PDP) were applied to interpret the final model.
A random forest model with 13 predictors (age, ethnicity, trauma type, intrusive memories, nightmares, reliving, distress, dissociation, cognitive avoidance, sleep, irritability, hypervigilance and startle) yielded F-scores of.973,0.902 and.961 with training and two external datasets. Shapley values were calculated for individual and grouped predictors. A cumulative effect for intrusion symptoms was observed. PDP showed a non-linear relationship between age and PTSD, and between ASD symptom severity and PTSD. A 43 % difference in the risk between non-minority and minority ethnic groups was detected.
A ML model demonstrated excellent classification performance and good potential for clinical utility, using a few easily obtainable variables. Model interpretation gave a comprehensive quantitative analysis on the operations among predictors, in particular ASD symptoms.
接受医疗护理的青少年受伤后有患 PTSD 的风险。因此，需要在早期准确预测慢性 PTSD。机器学习 (ML) 提供了一种精确预测和解释的有前途的方法。
该研究提出了一种用于受伤后 6-12 个月 PTSD 的临床有用预测模型，分析了预测因素之间以及预测因素与结果之间的关系。
采用机器学习 (ML) 方法对 9 项前瞻性研究的 1167 名儿童和青少年进行了训练。将人口统计学、创伤特征和急性创伤应激 (ASD) 症状作为初始预测因素。使用 DSM-IV PTSD 诊断标准得出 6 个月时的 PTSD 诊断。在外部数据集中对模型进行验证。应用 Shapley 值和偏依赖图 (PDP) 对最终模型进行解释。
一个具有 13 个预测因素（年龄、种族、创伤类型、侵入性记忆、梦魇、重现、痛苦、分离、认知回避、睡眠、易怒、过度警觉和惊吓）的随机森林模型在训练数据和两个外部数据集中的 F 分数分别为.973、.902 和.961。为个体和分组预测因素计算了 Shapley 值。观察到侵入性症状的累积效应。PDP 显示了年龄和 PTSD 之间以及 ASD 症状严重程度和 PTSD 之间的非线性关系。在非少数民族和少数民族群体之间检测到 43%的风险差异。
使用少数易于获得的变量，ML 模型显示出出色的分类性能和良好的临床应用潜力。模型解释对预测因素之间的操作进行了全面的定量分析，特别是 ASD 症状。
