Lu Jiaying, Bu Pengju, Xia Xiaolin, Lu Ning, Yao Ling, Jiang Hou
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100101, China.
Environ Sci Pollut Res Int. 2021 Jun;28(23):29701-29709. doi: 10.1007/s11356-021-12658-7. Epub 2021 Feb 10.
The prediction of hospital emergency room visits (ERV) for respiratory diseases after the outbreak of PM is of great importance in terms of public health, medical resource allocation, and policy decision support. Recently, the machine learning methods bring promising solutions for ERV prediction in view of their powerful ability of short-term forecasting, while their performances still exist unknown. Therefore, we aim to check the feasibility of machine learning methods for ERV prediction of respiratory diseases. Three different machine learning models, including autoregressive integrated moving average (ARIMA), multilayer perceptron (MLP), and long short-term memory (LSTM), are introduced to predict daily ERV in urban areas of Beijing, and their performances are evaluated in terms of the mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R). The results show that the performance of ARIMA is the worst, with a maximum R of 0.70 and minimum MAE, RMSE, and MAPE of 99, 124, and 26.56, respectively, while MLP and LSTM perform better, with a maximum R of 0.80 (0.78) and corresponding MAE, RMSE, and MAPE of 49 (33), 62 (42), and 14.14 (9.86). In addition, it demonstrates that MLP cannot detect the time lag effect properly, while LSTM does well in the description and prediction of exposure-response relationship between PM pollution and infecting respiratory disease.
在颗粒物（PM）爆发后，对呼吸系统疾病的医院急诊室就诊（ERV）进行预测，对于公共卫生、医疗资源分配和政策决策支持而言至关重要。近来，机器学习方法凭借其强大的短期预测能力，为ERV预测带来了颇具前景的解决方案，但其性能仍不明朗。因此，我们旨在检验机器学习方法用于呼吸系统疾病ERV预测的可行性。引入了三种不同的机器学习模型，包括自回归积分移动平均（ARIMA）、多层感知器（MLP）和长短期记忆网络（LSTM），用于预测北京城区的每日ERV，并根据平均绝对误差（MAE）、均方根误差（RMSE）、平均绝对百分比误差（MAPE）和决定系数（R）对其性能进行评估。结果表明，ARIMA的性能最差，最大R值为0.70，MAE、RMSE和MAPE的最小值分别为99、124和26.56，而MLP和LSTM的性能更好，最大R值为0.80（0.78），相应的MAE、RMSE和MAPE分别为49（33）、62（42）和14.14（9.86）。此外，结果表明MLP不能正确检测时间滞后效应，而LSTM在描述和预测PM污染与感染性呼吸系统疾病之间的暴露 - 反应关系方面表现良好。
