The Multivariate Fusion Distribution Characteristics in Physician Demand Prediction
Aiming at the optimization of the big data infrastructure in China’s healthcare system, this study proposes a lightweight time series physician demand prediction model, which is especially suitable for the field of telemedicine. The model incorporates multi-head attention mechanisms and generates st...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/2/233 |
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| author | Jiazhen Zhang Wei Chen Xiulai Wang |
| author_facet | Jiazhen Zhang Wei Chen Xiulai Wang |
| author_sort | Jiazhen Zhang |
| collection | DOAJ |
| description | Aiming at the optimization of the big data infrastructure in China’s healthcare system, this study proposes a lightweight time series physician demand prediction model, which is especially suitable for the field of telemedicine. The model incorporates multi-head attention mechanisms and generates statistical information, which significantly improves the ability to process nonlinear data, adapt to different data sources, improve the computational efficiency, and process high-dimensional features. By combining variational autoencoders and LSTM units, the model can effectively capture complex nonlinear relationships and long-term dependencies, and the multi-head attention mechanism overcomes the limitations of traditional algorithms. This lightweight architecture design not only improves the computational efficiency but also enhances the stability in high-dimensional data processing and reduces feature redundancy by combining the normalization process with statistics. The experimental results show that the model has wide applicability and excellent performance in a telemedicine consulting service system. |
| format | Article |
| id | doaj-art-ed1e24928e80465f836a095806cecd90 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-ed1e24928e80465f836a095806cecd902025-01-24T13:39:50ZengMDPI AGMathematics2227-73902025-01-0113223310.3390/math13020233The Multivariate Fusion Distribution Characteristics in Physician Demand PredictionJiazhen Zhang0Wei Chen1Xiulai Wang2School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaAiming at the optimization of the big data infrastructure in China’s healthcare system, this study proposes a lightweight time series physician demand prediction model, which is especially suitable for the field of telemedicine. The model incorporates multi-head attention mechanisms and generates statistical information, which significantly improves the ability to process nonlinear data, adapt to different data sources, improve the computational efficiency, and process high-dimensional features. By combining variational autoencoders and LSTM units, the model can effectively capture complex nonlinear relationships and long-term dependencies, and the multi-head attention mechanism overcomes the limitations of traditional algorithms. This lightweight architecture design not only improves the computational efficiency but also enhances the stability in high-dimensional data processing and reduces feature redundancy by combining the normalization process with statistics. The experimental results show that the model has wide applicability and excellent performance in a telemedicine consulting service system.https://www.mdpi.com/2227-7390/13/2/233deep learningmulti-attention mechanismdemand forecastenhancement of datalong short-term memory network |
| spellingShingle | Jiazhen Zhang Wei Chen Xiulai Wang The Multivariate Fusion Distribution Characteristics in Physician Demand Prediction Mathematics deep learning multi-attention mechanism demand forecast enhancement of data long short-term memory network |
| title | The Multivariate Fusion Distribution Characteristics in Physician Demand Prediction |
| title_full | The Multivariate Fusion Distribution Characteristics in Physician Demand Prediction |
| title_fullStr | The Multivariate Fusion Distribution Characteristics in Physician Demand Prediction |
| title_full_unstemmed | The Multivariate Fusion Distribution Characteristics in Physician Demand Prediction |
| title_short | The Multivariate Fusion Distribution Characteristics in Physician Demand Prediction |
| title_sort | multivariate fusion distribution characteristics in physician demand prediction |
| topic | deep learning multi-attention mechanism demand forecast enhancement of data long short-term memory network |
| url | https://www.mdpi.com/2227-7390/13/2/233 |
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