Abstract
Meteorological factors, which are periodic and regular in a long run, have an unignorable impact on human health. Accurate health risk prediction based on meteorological factors is essential for optimal allocation of resource in healthcare units. However, due to the non-stationary and non-linear nature of the original hospitalization sequence, traditional methods are less robust in predicting it. This study aims to investigate hospital admission prediction models using time series pre-processing algorithms and deep learning approach based on meteorological factors. Using the electronic medical record data from Panyu Central Hospital and meteorological data of Panyu district from 2003 to 2019, 46,089 eligible patients with lower respiratory tract infections (LRTIs) and four meteorological factors were identified to build and evaluate the prediction models. A novel hybrid model, Cascade GAM-CEEMDAN-LSTM Model (CGCLM), was established in combination with generalized additive model (GAM), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and long-short term memory (LSTM) networks for predicting daily admissions of patients with LRTIs. The experimental results show that CGCLM multistep method proposed in this paper outperforms single LSTM model in the prediction of health risk time series at different time window sizes. Moreover, our results also indicate that CGCLM has the best prediction performance when the time window is set to 61 days (RMSE = 1.12, MAE = 0.87, R2 = 0.93). Adequate extraction of exposure-response relationships between meteorological factors and diseases and suitable handling of sequence pre-processing have an important role in time series prediction. This hybrid climate-based model for predicting LRTIs disease can also be extended to time series prediction of other epidemic disease.
Highlights
Climate change causes or aggravates a wide range of exposures with multiple impacts on health, both direct and indirect(Linares, et al, 2020)
LRTIs are a broad description of a group of of respiratory diseases that comprise acute bronchitis, pneumonia, and exacerbations of chronic lung disease(Woodhead, et al, 2011) and it has become the fourth most common cause of death according to the World
The results demonstrated that temperature, relative humidity, atmospheric pressure and wind velocity were correlated with LRTIs admission, with the maximum relative risk (RR) of lag[5] (RR= 1.015, 95%CI, 1.001 to 1.03),lag[0] (RR=1.07, 95%CI, 1.058 to 1.83), lag[0] (RR=0.978, 95%CI, 0.964 to 0.991) and lag[3] (RR=1.026, 95%CI, 1.014 to 1.039), respectively
Summary
Climate change causes or aggravates a wide range of exposures with multiple impacts on health, both direct and indirect(Linares, et al, 2020). In the context of continuing global climate change, identifying factors influencing the occurrence of LRTIs could contribute to the prediction of future outbreaks and facilitate the development of transmission prevention measures. A study conducted in Finland using a generalized additive model found that Low temperature and low relative humidity associated with increased incidence of LRTIs(Mäkinen, et al, 2009). These statistical regression models can assess the relationship between meteorological factors and the incidence of LRTIs, they generally do not provide sufficiently accurate incidence forecasts for medical management
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