Research center of l'Institut universitaire en santé mentale de Montréal (CR-IUSMM), Montréal, Québec, Canada.
School of Psychoeducation, University of Montreal, Université de Montréal Pavillon Marie-Victorin École de psychoéducation, C. P. 6128, succursale Centre-ville, Montréal, Québec, H3C 3J7, Canada.
J Med Syst. 2023 Nov 16;47(1):120. doi: 10.1007/s10916-023-02011-5.
The purpose of this study was to train and test preliminary models using two machine learning algorithms to identify healthcare workers at risk of developing anxiety, depression, and post-traumatic stress disorder. The study included data from a prospective cohort study of 816 healthcare workers collected using a mobile application during the first two waves of COVID-19. Each week, the participants responded to 11 questions and completed three screening questionnaires (one for anxiety, one for depression, and one for post-traumatic stress disorder). Then, the research team selected two questions (out of the 11), which were used with biological sex to identify whether scores on each screening questionnaire would be positive or negative. The analyses involved a fivefold cross-validation to test the accuracy of models based on logistic regression and support vector machines using cross-sectional and cumulative measures. The findings indicated that the models derived from the two questions and biological sex accurately identified screening scores for anxiety, depression, and post-traumatic stress disorders in 70% to 80% of cases. However, the positive predictive value never exceeded 50%, underlining the importance of collecting more data to train better models. Our proof of concept demonstrates the feasibility of using machine learning to develop novel models to screen for psychological distress in at-risk healthcare workers. Developing models with fewer questions may reduce burdens of active monitoring in practical settings by decreasing the weekly assessment duration.
本研究旨在使用两种机器学习算法训练和测试初步模型，以识别有发生焦虑、抑郁和创伤后应激障碍风险的医护人员。该研究的数据来自一项前瞻性队列研究，共纳入了 816 名医护人员，他们在 COVID-19 的前两波疫情期间使用移动应用程序进行了数据收集。每周，参与者需要回答 11 个问题，并完成三个筛查问卷（一个用于焦虑，一个用于抑郁，一个用于创伤后应激障碍）。然后，研究团队从这 11 个问题中选择了两个问题，并结合生物性别，以确定每个筛查问卷的分数是阳性还是阴性。分析采用五重交叉验证，以测试基于逻辑回归和支持向量机的模型的准确性，使用横断面和累积测量值。研究结果表明，基于这两个问题和生物性别建立的模型可以准确识别 70%至 80%案例的焦虑、抑郁和创伤后应激障碍筛查评分。然而，阳性预测值从未超过 50%，这强调了收集更多数据以训练更好模型的重要性。我们的概念验证证明了使用机器学习开发新模型来筛查高危医护人员心理困扰的可行性。通过减少每周评估的时长，开发包含较少问题的模型可能会减少实际环境中主动监测的负担。
