Prediction of outpatient visits for allergic rhinitis using an artificial intelligence LSTM model - a study in Eastern China

BMC Public Health, Год журнала: 2025, Номер 25(1)

Опубликована: Апрель 9, 2025

Allergic rhinitis is a common disease that can affect the health of patients and bring huge social economic burdens. In this study, we developed model to predict incidence rate allergic so as provide accurate information for treatment, prevention, control rhinitis. We Long Short-Term Memory effectively predicting daily outpatient visits based on air pollution meteorological data. collected data from departments otolaryngology, emergency medicine, pediatrics, respiratory medicine at Affiliated Hospital Hangzhou Normal University, January 2022 August 2024. The were stratified by gender age separately input into evaluation. A total 25,425 samples assessed in study. Based obtained males (n = 13,943), females 11,482), adults 17,473), minors 7,952), normalized mean squared errors 0.4674976, 0.3812502, 0.418301, 0.4322124, respectively. By comparing NMSE prediction results ARIMA LSTM models dataset, was found outperform terms stability accuracy. presented here could data, thereby offering valuable data-driven support hospital management potentially improving societal prevention

Язык: Английский

Agricultural GDP exposure to drought and its machine learning-based prediction in the Jialing River Basin, China

Agricultural Water Management, Год журнала: 2024, Номер 307, С. 109265 - 109265

Опубликована: Дек. 30, 2024

Язык: Английский