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Paper details
Number 1 - March 2023
Volume 33 - 2023
Hospitalization patient forecasting based on multi-task deep learning
Min Zhou, Xiaoxiao Huang, Haipeng Liu, Dingchang Zheng
Abstract
Forecasting the number of hospitalization patients is important for hospital management. The number of hospitalization
patients depends on three types of patients, namely, admission patients, discharged patients, and inpatients. However,
previous works focused on one type of patients rather than the three types of patients together. In this paper, we propose
a multi-task forecasting model to forecast the three types of patients simultaneously. We integrate three neural network
modules into a unified model for forecasting. Besides, we extract date features of admission and discharged patient flows to
improve forecasting accuracy. The algorithm is trained and evaluated on a real-world data set of a one-year daily observation
of patient numbers in a hospital. We compare the performance of our model with eight baselines over two real-word data
sets. The experimental results show that our approach outperforms other baseline algorithms significantly.
Keywords
hospitalization patients, forecasting, neural network, multitask learning