Prediction of Mental Health in Medical Workers During COVID-19 Based on Machine Learning

Abstract

Mental health prediction is one of the most essential parts of reducing the probability of serious mental illness. Meanwhile, mental health prediction can provide a theoretical basis for public health department to work out psychological intervention plans for medical workers. The purpose of this paper is to predict mental health of medical workers based on machine learning by 32 factors. We collected the 32 factors of 5,108 Chinese medical workers through questionnaire survey, and the results of Self-reporting Inventory was applied to characterize mental health. In this study, we propose a novel prediction model based on optimization algorithm and neural network, which can select and rank the most important factors that affect mental health of medical workers. Besides, we use stepwise logistic regression, binary bat algorithm, hybrid improved dragonfly algorithm and the proposed prediction model to predict mental health of medical workers. The results show that the prediction accuracy of the proposed model is 92.55%, which is better than the existing algorithms. This method can be used to predict mental health of global medical worker. In addition, the method proposed in this paper can also play a role in the appropriate work plan for medical worker.

A Self-Stabilizing and Parameter-Optimized Deep Learning Model for Predicting Mental Health Disorder

“…The dataset description is given in section 4. The developed model performance is assessed with various existing detection techniques like IGCBA-BPNN [16], CNN-RNN [18] CNN-Bi-LSTM [19] CSI-MLP [10]. For determine the performance efficiency, the proposed and existing methods are evaluated with different metrics which are listed below.…”

“…A comprehensive prediction system called improved global chaos bat back propagation neural network (IGCBA-BPNN) was created [16] to determine and classify the most relevant elements influencing medical professionals' mental health. IGCBA-BPNN not only enhances MHD prediction accuracy, but it also chooses the smallest feature variables.…”

Knowledge atlas on the medical artificial intelligence research: a bibliometric analysis based on CiteSpace and VOSviewer

“…Gao W [68] built a new multimode AI system including feature engineering, machine learning and deep learning based on abdominal X-ray pictures and clinical data, which can help clinicians improve diagnosis efficiency, reduce missed diagnosis times, promote early diagnosis and treatment, and prevent disease progression and even death. Wang X [69] put forward a prediction model based on optimization algorithm and neural network, which can select and sort the most important factors affecting the mental health of medical staff, predict the mental health status of medical staff around the world, and help to make appropriate work plans for medical staff.…”

A machine learning approach to predict resilience and sickness absence in the healthcare workforce during the COVID-19 pandemic

“…During the pandemic, ML techniques have been harnessed to predict COVID-19 infection based on symptoms and imaging findings with very high accuracy 13 , 14 . Nevertheless, while a few efforts to predict HCWs' mental health using ML at a particular timepoint of the pandemic have been made 15 , 16 , no previous work has used ML to identify HCWs who remain poorly resilient to COVID-19-related distress throughout the pandemic. Previous work has shown that local increases in incidence rates of COVID-19 cases are reflected in psychological distress and sleeping problems in HCWs 17 .…”