ABSTRACT
Objective To study the effect of meteorological factors on the number of hypertension outpatients in four areas of Gansu Province, then predict and analyze the trend of the number of hypertension outpatients, so as to provide reference for the prevention and control of hypertension diseases. Methods On the basis of controlling the confounding factors such as long-term trends, date effects, meteorological information and contaminant influence, a mixed model of convolutional neural network (CNN) and long-short term memory (LSTM) was constructed for the number of hypertension outpatients in the four regions of Baiyin, Chengxian, Qingcheng and Liangzhou by Python programming language. Results The root mean square errors of the CNN-LSTM model for the number of hypertensive outpatients in the four regions was 6.330 9, 6.814 2, 6.393 6 and 6.867 6. The mean absolute percentage error was 74.082 2, 78.508 2, 56.618 3 and 50.235 4. And the average absolute errors was 4.875 7, 5.431 1, 4.542 0 and 6.460 8. All the results was superior to those of support vector machine (SVM), autoregressive integrated moving average model (ARIMA), random forest (RF), CNN and LSTM. Conclusion The CNN-LSTM model can accurately predict the number of hypertension outpatients in Gansu. The hospital can rationally allocate medical resources according to the needs of hypertension for medical treatment at different times.
ABSTRACT
Objective To investigate the effect of meteorological factors on the number of outpatients with pulmonary heart disease in Liangzhou district of Gansu province. Methods We collected the daily meteorological data (temperature, air pressure, precipitation, sunshine hours, etc.) of Liangzhou district of Gansu province and the number of daily outpatients with the pulmonary heart disease from 2014 to 2016, and used the distribution lag model to analyze the impact relationship and hysteresis effect of the meteorological factors on the number of outpatients to pulmonary heart disease clinics. Results The total number of outpatients with pulmonary heart disease was 20 462 in Liangzhou district from 2014 to 2016, and the average number of outpatients per day was 18.67. The number of outpatients with pulmonary heart disease per day was positively correlated with temperature and sunshine hours, and negatively correlated with air pressure, relative humidity and precipitation. Among them, the average daily temperature had the most significant effect on the number of outpatients with pulmonary heart disease (r=0.133, P<0.001). At the highest daily average temperature, lagging 16 days,the relative risk coefficient (RR value) was the highest (1.26, 95% CI:1.13-1.40). For every 1 ℃ increase in temperature, the number of outpatients with pulmonary heart disease increased by 1.26 (95% CI: 1.13-1.40). There was no risk of morbidity at an extreme low temperature (-18 ℃), and the relative risk of the number of the pulmonary heart disease outpatients was the greatest at lag 0-15 at an extreme high temperatures (29 ℃). Conclusion Meteorological factor is an important factor affecting the number of outpatients with pulmonary heart disease in Liangzhou district. The risk of pulmonary heart disease will increase due to temperature changes, and the impact will occur immediately on the same day. The high temperature effect is short-lived and the relative risk is high, while the relative risk of low temperature to the number of outpatients is relatively low and the lag time is long.