Niuvanniemi Hospital, University of Eastern Finland, Kuopio, Finland.
Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Sci Rep. 2022 May 16;12(1):8055. doi: 10.1038/s41598-022-12107-6.
During the COVID-19 pandemic, healthcare workers (HCWs) have faced unprecedented workloads and personal health risks leading to mental disorders and surges in sickness absence. Previous work has shown that interindividual differences in psychological resilience might explain why only some individuals are vulnerable to these consequences. However, no prognostic tools to predict individual HCW resilience during the pandemic have been developed. We deployed machine learning (ML) to predict psychological resilience during the pandemic. The models were trained in HCWs of the largest Finnish hospital, Helsinki University Hospital (HUS, N = 487), with a six-month follow-up, and prognostic generalizability was evaluated in two independent HCW validation samples (Social and Health Services in Kymenlaakso: Kymsote, N = 77 and the City of Helsinki, N = 322) with similar follow-ups never used for training the models. Using the most predictive items to predict future psychological resilience resulted in a balanced accuracy (BAC) of 72.7-74.3% in the HUS sample. Similar performances (BAC = 67-77%) were observed in the two independent validation samples. The models' predictions translated to a high probability of sickness absence during the pandemic. Our results provide the first evidence that ML techniques could be harnessed for the early detection of COVID-19-related distress among HCWs, thereby providing an avenue for potential targeted interventions.
在 COVID-19 大流行期间，医护人员（HCWs）面临着前所未有的工作量和个人健康风险，导致精神障碍和病假激增。先前的研究表明，心理弹性的个体差异可能解释了为什么只有一些人容易受到这些后果的影响。然而，目前还没有预测工具可以预测大流行期间个体 HCW 的适应能力。我们使用机器学习（ML）来预测大流行期间的心理适应能力。这些模型是在芬兰最大的医院赫尔辛基大学医院（HUS）的 HCWs 中进行训练的，随访时间为六个月，并在两个独立的 HCW 验证样本（社会和健康服务在凯米拉约基：Kymsote，N=77 和赫尔辛基市，N=322）中评估了预后的泛化能力，这些样本的随访时间与从未用于训练模型的时间相似。使用最具预测性的项目来预测未来的心理适应能力，在 HUS 样本中得出了 72.7-74.3%的平衡准确性（BAC）。在两个独立的验证样本中也观察到了类似的性能（BAC=67-77%）。这些模型的预测结果表明，在大流行期间，病假的可能性很高。我们的研究结果首次提供了证据，证明 ML 技术可以用于早期检测 HCWs 与 COVID-19 相关的困扰，从而为潜在的有针对性的干预提供了途径。
