Prediction of the number of asthma patients using environmental factors based on deep learning algorithms

Abstract

Background: Air pollution, weather, pollen, and influenza are typical aggravating factors for asthma. Previous studies have identified risk factors using regression-based and ensemble models. However, studies that consider complex relationships and interactions among these factors have yet to be conducted. Although deep learning algorithms can address this problem, further research on modeling and interpreting the results is warranted. Methods: In this study, from 2015 to 2019, information about air pollutants, weather conditions, pollen, and influenza were utilized to predict the number of emergency room patients and outpatients with asthma using recurrent neural network, long short-term memory (LSTM), and gated recurrent unit models. The relative importance of the environmental factors in asthma exacerbation was quantified through a feature importance analysis. Results: We found that LSTM was the best algorithm for modeling patients with asthma. Our results demonstrated that influenza, temperature, PM10, NO2, CO, and pollen had a significant impact on asthma exacerbation. In addition, the week of the year and the number of holidays per week were an important factor to model the seasonality of the number of asthma patients and the effect of holiday clinic closures, respectively. Conclusion: LSTM is an excellent algorithm for modeling complex epidemiological relationships, encompassing nonlinearity, lagged responses, and interactions. Our study findings can guide policymakers in their efforts to understand the environmental factors of asthma exacerbation.