- Tytuł:
- Hospitalization patient forecasting based on multi-task deep learning
- Autorzy:
-
Zhou, Min
Huang, Xiaoxiao
Liu, Haipeng
Zheng, Dingchang - Powiązania:
- https://bibliotekanauki.pl/articles/2201025.pdf
- Data publikacji:
- 2023
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
hospitalization patient
neural network
multitask learning
pacjent hospitalizowany
sieć neuronowa
nauka wielozadaniowa - Opis:
- 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.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2023, 33, 1; 151--162
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł