Hospitalization Patient Forecasting Based on Multi–Task Deep Learning

Min Zhou, Xiaoxiao Huang, Haipeng Liu, Dingchang Zheng

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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.
Original languageEnglish
Pages (from-to)151-162
Number of pages12
JournalInternational Journal of Applied Mathematics and Computer Science
Issue number1
Publication statusPublished - 1 Mar 2023

Bibliographical note

This work is licensed under the Creative Commons Attribution-Non Commercial-No Derivatives 3.0 License


  • hospitalization patients
  • forecasting
  • neural network
  • multitask learning


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