Objective To assess the short-term lag effects of climate and air pollution on hospital admissions for cardiovascular and respiratory diseases,and to develop deep learning-based models for daily hospital admission pre...Objective To assess the short-term lag effects of climate and air pollution on hospital admissions for cardiovascular and respiratory diseases,and to develop deep learning-based models for daily hospital admission prediction.Methods A multi-city study was conducted in Tokyo’s 23 wards,Osaka City,and Nagoya City.Random forest models were employed to assess the synergistic short-term lag effects(lag0,lag3,and lag7)of climate and air pollutants on hospitalization for five cardiovascular diseases(CVDs)and two respiratory diseases(RDs).Furthermore,we developed hybrid deep learning models that integrated an autoencoder(AE)with a Long Short-Term Memory network(AE+LSTM)to predict daily hospital admissions.Results On the day of exposure(lag0),air pollutants,particularly nitrogen oxides(NOx),exhibited the strongest influence on hospital admissions for CVD and RD,with pronounced effects observed for hypertension(I10–I15),ischemic heart disease(I20),arterial and capillary diseases(I70–I79),and lower respiratory infections(J20–J22 and J40–J47).At longer lags(lag3 and lag7),temperature and precipitation were more influential predictors.The AE+LSTM model outperformed the standard LSTM,improving the prediction accuracy by 32.4%for RD in Osaka and 20.94%for CVD in Nagoya.Conclusion Our findings reveal the dynamic,time-varying health risks associated with environmental exposure and demonstrate the utility of deep learnings in predicting short-term hospital admissions.This framework can inform early warning systems,enhance healthcare resource allocation,and support climate-adaptive public health strategies.展开更多
基金supported by the Japan Science and Technology Agency SPRING Program(JST SPRING),Grant Number JPMJSP2108,which was partially funded by the Japan Society for the Promotion of Science(JSPS)Grant Numbers 20H03949,23K22919,23K28289the Environmental Restoration and Conservation Agency of Japan,and the Environment Research and Technology Development Fund(S-24).
文摘Objective To assess the short-term lag effects of climate and air pollution on hospital admissions for cardiovascular and respiratory diseases,and to develop deep learning-based models for daily hospital admission prediction.Methods A multi-city study was conducted in Tokyo’s 23 wards,Osaka City,and Nagoya City.Random forest models were employed to assess the synergistic short-term lag effects(lag0,lag3,and lag7)of climate and air pollutants on hospitalization for five cardiovascular diseases(CVDs)and two respiratory diseases(RDs).Furthermore,we developed hybrid deep learning models that integrated an autoencoder(AE)with a Long Short-Term Memory network(AE+LSTM)to predict daily hospital admissions.Results On the day of exposure(lag0),air pollutants,particularly nitrogen oxides(NOx),exhibited the strongest influence on hospital admissions for CVD and RD,with pronounced effects observed for hypertension(I10–I15),ischemic heart disease(I20),arterial and capillary diseases(I70–I79),and lower respiratory infections(J20–J22 and J40–J47).At longer lags(lag3 and lag7),temperature and precipitation were more influential predictors.The AE+LSTM model outperformed the standard LSTM,improving the prediction accuracy by 32.4%for RD in Osaka and 20.94%for CVD in Nagoya.Conclusion Our findings reveal the dynamic,time-varying health risks associated with environmental exposure and demonstrate the utility of deep learnings in predicting short-term hospital admissions.This framework can inform early warning systems,enhance healthcare resource allocation,and support climate-adaptive public health strategies.
