Deep learning for predicting the occurrence of cardiopulmonary diseases in Nanjing, China. (October 2020)

• Record Type: Journal Article
• Authors: Wang, Ce; Qi, Yi; Zhu, Guangcan
• Is Part Of: Chemosphere. Volume 257(2020)
• Publication Date: 2020-10
• Subjects: Deep learning -- LSTM -- Respiratory -- Cardiovascular -- Air pollution; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2020.127176
• Languages: English
• ISSNs: 0045-6535

Abstract

Abstract: The efficiency of disease prevention and medical care service necessitated the prediction of incidence. However, predictive accuracy and power were largely impeded in a complex system including multiple environmental stressors and health outcome of which the occurrence might be episodic and irregular in time. In this study, we established four different deep learning (DL) models to capture inherent long-term dependencies in sequences and potential complex relationships among constituents by initiating with the original input into a representation at a higher abstract level. We collected 504, 555 and 786, 324 hospital outpatient visits of grouped categories of respiratory (RESD) and circulatory system disease (CCD), respectively, in Nanjing from 2013 through 2018. The matched observations in time-series that might pose risk to cardiopulmonary health involved conventional air pollutants concentrations and metrological conditions. The results showed that a well-trained network architecture built upon long short-term memory block and a working day enhancer achieved optimal performance by three quantitative statistics, i.e., 0.879 and 0.902 of Nash-Sutcliffe efficiency, 0.921% and 0.667% of percent bias, and 0.347 and 0.312 of root mean square error-standard deviation ratio for RESD and CCD hospital visits, respectively. We observed the non-linear association of nitrogen dioxide and ambient air temperature with CCD hospital visits. Furthermore, these two environmental stressors were identified as the most sensitive predictive variables, and exerted synergetic effect for two health outcomes, particular in winter season. Our study indicated that high-quality surveillance data of atmospheric environments could provide novel opportunity for anticipating temporal trend of cardiopulmonary health outcomes based on DL model. Graphical abstract: Image 1 Highlights: A deep learning model was established to predict the evolution of health outcomes. Air pollutants concentrations and meteorological conditions were used as predictors. Association of NO2 and air temperature with cardiovascular diseases was non-linear. Nitrogen dioxide and air temperature had synergetic effect on cardiopulmonary health.