<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

Hospitalization Patient Forecasting Based on Multi–Task Deep Learning

The First Affiliated Hospital, Zhejiang University School of Medicine

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Hospitalization Patient Forecasting Based on Multi\u2013Task Deep Learning"}

Forecasting the number of hospitalization patients is important for hospital management. The number of hospitalization patients depends on three types of...

Hospitalization Patient Forecasting Based on Multi–Task Deep Learning

Data publikacji: 29 mar 2023

Zakres stron: 151 - 162

Otrzymano: 23 sty 2022

Przyjęty: 09 sie 2022

DOI: https://doi.org/10.34768/amcs-2023-0012

Słowa kluczowe
hospitalization patients, forecasting, neural network, multitask learning

© 2023 Min Zhou et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Hospitalization Patient Forecasting Based on Multi–Task Deep Learning

Data publikacji: 29 mar 2023

Zakres stron: 151 - 162

Otrzymano: 23 sty 2022

Przyjęty: 09 sie 2022

DOI: https://doi.org/10.34768/amcs-2023-0012

Słowa kluczowehospitalization patients, forecasting, neural network, multitask learning

© 2023 Min Zhou et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
hospitalization patients, forecasting, neural network, multitask learning