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The Multivariate Fusion Distribution Characteristics in Physician Demand Prediction

Author

Listed:
  • Jiazhen Zhang

    (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Wei Chen

    (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Xiulai Wang

    (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

Abstract

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.

Suggested Citation

  • Jiazhen Zhang & Wei Chen & Xiulai Wang, 2025. "The Multivariate Fusion Distribution Characteristics in Physician Demand Prediction," Mathematics, MDPI, vol. 13(2), pages 1-22, January.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:2:p:233-:d:1564904
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    References listed on IDEAS

    as
    1. Wang, Kai & Li, Kangnan & Du, Feng & Zhang, Xiang & Wang, Yanhai & Sun, Jiazhi, 2024. "Research on prediction model of coal spontaneous combustion temperature based on SSA-CNN," Energy, Elsevier, vol. 290(C).
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