Center for Biomedical Informatics, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Sci Rep. 2023 Apr 12;13(1):6003. doi: 10.1038/s41598-023-33222-y.
The COVID-19 pandemic is a global health concern that has spread around the globe. Machine Learning is promising in the fight against the COVID-19 pandemic. Machine learning and artificial intelligence have been employed by various healthcare providers, scientists, and clinicians in medical industries in the fight against COVID-19 disease. In this paper, we discuss the impact of the Covid-19 pandemic on alcohol consumption habit changes among healthcare workers in the United States during the first wave of the Covid-19 pandemic. We utilize multiple supervised and unsupervised machine learning methods and models such as decision trees, logistic regression, support vector machines, multilayer perceptron, XGBoost, CatBoost, LightGBM, AdaBoost, Chi-Squared Test, mutual information, KModes clustering and the synthetic minority oversampling technique on a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research to investigate the links between COVID-19-related deleterious effects and changes in alcohol consumption habits among healthcare workers. Through the interpretation of the supervised and unsupervised methods, we have concluded that healthcare workers whose children stayed home during the first wave in the US consumed more alcohol. We also found that the work schedule changes due to the Covid-19 pandemic led to a change in alcohol use habits. Changes in food consumption, age, gender, geographical characteristics, changes in sleep habits, the amount of news consumption, and screen time are also important predictors of an increase in alcohol use among healthcare workers in the United States.
COVID-19 大流行是一个全球性的健康问题，已经在全球范围内蔓延。机器学习在抗击 COVID-19 大流行方面具有广阔的前景。机器学习和人工智能已被医疗行业的各种医疗保健提供者、科学家和临床医生用于抗击 COVID-19 疾病。在本文中，我们讨论了 COVID-19 大流行对美国医疗保健工作者在 COVID-19 大流行第一波期间饮酒习惯变化的影响。我们利用了多种监督和无监督的机器学习方法和模型，如决策树、逻辑回归、支持向量机、多层感知机、XGBoost、CatBoost、LightGBM、AdaBoost、卡方检验、互信息、KMode 聚类和合成少数过采样技术，对从密歇根大学大学间政治和社会研究联合会获得的心理健康调查数据进行了分析，以研究 COVID-19 相关有害影响与医疗保健工作者饮酒习惯变化之间的联系。通过对监督和无监督方法的解释，我们得出结论，在美国第一波疫情期间孩子呆在家里的医疗保健工作者饮酒量更多。我们还发现，由于 COVID-19 大流行导致的工作时间表变化导致了饮酒习惯的改变。饮食习惯的改变、年龄、性别、地理特征、睡眠习惯的改变、新闻消费的数量以及屏幕时间也是美国医疗保健工作者饮酒量增加的重要预测因素。
