Hospitalization patient forecasting based on multi-task deep learning

• Autorzy: Zhou Min , Huang Xiaoxiao , Liu Haipeng , Zheng Dingchang

Identyfikatory

DOI

10.34768/amcs-2023-0012

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

hospitalization patient; neural network; multitask learning

PL

pacjent hospitalizowany; sieć neuronowa; nauka wielozadaniowa

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2023
• Tom: Vol. 33, no. 1
• Strony: 151--162
• Opis fizyczny: Bibliogr. 34 poz., rys., tab., wykr.

Twórcy

autor

Zhou Min

• The First Affiliated Hospital, Zhejiang University School of Medicine, No.79 Qingchun Rd., 310003, Hangzhou, China

autor

Huang Xiaoxiao

• The First Affiliated Hospital, Zhejiang University School of Medicine, No.79 Qingchun Rd., 310003, Hangzhou, China

autor

Liu Haipeng

• Research Centre for Intelligent Healthcare, Coventry University, Priory Street, CV1 5FB, Coventry, UK

autor

Zheng Dingchang

• Research Centre for Intelligent Healthcare, Coventry University, Priory Street, CV1 5FB, Coventry, UK

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)