Objectives: Firefighters are prone to suffer from psychological trauma and post-traumatic stress disorder (PTSD) in the workplace, and have a poor prognosis after PTSD. Reliable models for predicting PTSD allow for effective identification and intervention for patients with early PTSD. By collecting the psychological traits, psychological states and work situations of firefighters, this study aims to develop a machine learning algorithm with the aim of effectively and accurately identifying the onset of PTSD in firefighters, as well as detecting some important predictors of PTSD onset.
Methods: This study conducted a cross-sectional survey through convenient sampling of firefighters from 20 fire brigades in Changsha, which were evenly distributed across 6 districts and Changsha County, with a total of 628 firefighters. We used the synthetic minority oversampling technique (SMOTE) to process data sets and used grid search to finish the parameter tuning. The predictive capability of several commonly used machine learning models was compared by 5-fold cross-validation and using the area under the receiver operating characteristic curve (ROC-AUC), accuracy, precision, recall, and F1 score.
Results: The random forest model achieved good performance in predicting PTSD with an average AUC score at 0.790. The mean accuracy of the model was 90.1%, with an F1 score of 0.945. The three most important predictors were perseverance, forced thinking, and reflective deep thinking, with weights of 0.165, 0.158, and 0.152, respectively. The next most important predictors were employment time, psychological power, and optimism.
Conclusions: PTSD onset prediction model for Changsha firefighters constructed by random forest has strong predictive ability, and both psychological characteristics and work situation can be used as predictors of PTSD onset risk for firefighters. In the next step of the study, validation using other large datasets is needed to ensure that the predictive models can be used in clinical setting.
目的: 消防员极易发生职业心理创伤和创伤后应激障碍(post‐traumatic stress disorder，PTSD)，且患PTSD后的预后较差。预测PTSD的可靠模型可对早期PTSD患者进行有效识别。本研究通过收集消防员的心理特质、心理状态和工作情况，旨在开发一种机器学习算法，以期有效和准确地识别消防员PTSD的发病情况，同时探索PTSD发病的一些重要预测因子。方法: 通过方便抽样对长沙市的6个区和长沙县20个消防队的628名消防员进行问卷调查。收集长沙市消防员的人口学资料、工作情况、身体状况、心理弹性量表和反刍思维量表评分等，采用合成少数类过采样技术(synthetic minority oversampling technique，SMOTE)来处理数据集，使用网格搜索进行超参数调优。通过5折交叉验证，并采用受试者操作特征(receiver operator characteristic，ROC)的曲线下面积(area under the curve，AUC)、准确度、精确率、召回率和F1分数比较多种常用机器学习模型的预测能力。结果: 随机森林模型在预测PTSD方面具有较高的预测能力。随机森林模型平均AUC为0.790，模型的平均精确率为90.1%，F1分数为0.945。权重最大的3个预测因素为坚韧性、强迫思考和反省深思，权重分别为0.165、0.158和0.152。其次为从业时间、力量性和乐观性。结论: 通过随机森林构建的长沙消防员患PTSD预测模型具有较强的预测能力，心理特征和工作情况是消防员患PTSD风险的预测因子。但需要使用其他大型数据集进行验证，以确保预测模型能够用于临床实践。.
Keywords: firefighter; machine learning algorithm; post-traumatic stress disorder; predictor.