Prediction of outpatients with conjunctivitis in Xinjiang based on LSTM and GRU models

• Published in: PloS one 2023-09, Vol.18 (9), p.e0290541-e0290541
• Main Authors: Wang, Yijia, Yi, Xianglong, Luo, Mei, Wang, Zhe, Qin, Long, Hu, Xijian, Wang, Kai
• Format: Article
• Language: English
• Subjects: Analysis; Artificial intelligence; Biology and Life Sciences; Care and treatment; Computer and Information Sciences; Conjunctivitis; Deep learning; Diagnosis; Disease; Forecast accuracy; Hospitals; Long short-term memory; Machine learning; Medical research; Medicine and Health Sciences; Modelling; Neural networks; Neurons; Ophthalmology; Physical Sciences; Predictions; Research and Analysis Methods; Root-mean-square errors
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0290541

Background Reasonable and accurate forecasting of outpatient visits helps hospital managers optimize the allocation of medical resources, facilitates fine hospital management, and is of great significance in improving hospital efficiency and treatment capacity. Methods Based on conjunctivitis outpatient data from the First Affiliated Hospital of Xinjiang Medical University Ophthalmology from 2017/1/1 to 2019/12/31, this paper built and evaluated Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models for outpatient visits prediction. Results In predicting the number of conjunctivitis visits over the next 31 days, the LSTM model had a root mean square error (RMSE) of 2.86 and a mean absolute error (MAE) of 2.39, the GRU model has an RMSE of 2.60 and an MAE of 1.99. Conclusions The GRU method can better predict trends in hospital outpatient flow over time, thus providing decision support for medical staff and outpatient management.