Biomedical Engineering Program, Faculty of Medicine-Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Beirut, Lebanon.
Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium.
Hum Resour Health. 2022 Aug 19;20(1):63. doi: 10.1186/s12960-022-00758-5.
Maternal and newborn healthcare providers are essential professional groups vulnerable to physical and psychological risks associated with the COVID-19 pandemic. This study uses machine learning algorithms to create a predictive tool for maternal and newborn healthcare providers' perception of being safe in the workplace globally during the pandemic.
We used data collected between 24 March and 5 July 2020 through a global online survey of maternal and newborn healthcare providers. The questionnaire was available in 12 languages. To predict healthcare providers' perception of safety in the workplace, we used features collected in the questionnaire, in addition to publicly available national economic and COVID-19-related factors. We built, trained and tested five machine learning models: Support Vector Machine (SVM), Random Forest (RF), XGBoost, CatBoost and Artificial Neural Network (ANN) for classification and regression. We extracted from RF models the relative contribution of features in output prediction.
Models included data from 941 maternal and newborn healthcare providers from 89 countries. ML models performed well in classification and regression tasks, whereby RF had 82% cross-validated accuracy for classification, and CatBoost with 0.46 cross-validated root mean square error for regression. In both classification and regression, the most important features contributing to output prediction were classified as three themes: (1) information accessibility, clarity and quality; (2) availability of support and means of protection; and (3) COVID-19 epidemiology.
This study identified salient features contributing to maternal and newborn healthcare providers perception of safety in the workplace. The developed tool can be used by health systems globally to allow real-time learning from data collected during a health system shock. By responding in real-time to the needs of healthcare providers, health systems could prevent potential negative consequences on the quality of care offered to women and newborns.
孕产妇和新生儿医护人员是应对与 COVID-19 大流行相关的身心风险的关键专业群体。本研究采用机器学习算法，为全球大流行期间孕产妇和新生儿医护人员对工作场所安全的感知创建预测工具。
我们使用了 2020 年 3 月 24 日至 7 月 5 日期间通过全球在线孕产妇和新生儿医护人员调查收集的数据。问卷有 12 种语言版本。为了预测医护人员对工作场所安全的感知，我们除了使用问卷中收集的特征外，还使用了公开的国家经济和 COVID-19 相关因素。我们构建、训练和测试了五个机器学习模型：支持向量机（SVM）、随机森林（RF）、XGBoost、CatBoost 和人工神经网络（ANN）用于分类和回归。我们从 RF 模型中提取特征对输出预测的相对贡献。
模型纳入了来自 89 个国家的 941 名孕产妇和新生儿医护人员的数据。机器学习模型在分类和回归任务中表现良好，RF 模型的分类交叉验证准确率为 82%，CatBoost 模型的回归交叉验证均方根误差为 0.46。在分类和回归中，对输出预测贡献最大的特征分为三个主题：（1）信息可及性、清晰度和质量；（2）支持和保护手段的可用性；（3）COVID-19 流行病学。
本研究确定了孕产妇和新生儿医护人员对工作场所安全感知的重要特征。开发的工具可被全球卫生系统用于允许从卫生系统冲击期间收集的数据实时学习。通过实时响应医护人员的需求，卫生系统可以防止对向妇女和新生儿提供的护理质量产生潜在的负面影响。
