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Healthcare Sustainability: Hospitalization Rate Forecasting with Transfer Learning and Location-Aware News Analysis

Author

Listed:
  • Jing Chen

    (Stevens Institute of Technology, Hoboken, NJ 07030, USA)

  • Germán G. Creamer

    (Stevens Institute of Technology, Hoboken, NJ 07030, USA)

  • Yue Ning

    (Stevens Institute of Technology, Hoboken, NJ 07030, USA)

  • Tal Ben-Zvi

    (Stevens Institute of Technology, Hoboken, NJ 07030, USA)

Abstract

Monitoring and forecasting hospitalization rates are of essential significance to public health systems in understanding and managing overall healthcare deliveries and strategizing long-term sustainability. Early-stage prediction of hospitalization rates is crucial to meet the medical needs of numerous patients during emerging epidemic diseases such as COVID-19. Nevertheless, this is a challenging task due to insufficient data and experience. In addition, relevant existing work neglects or fails to exploit the extensive contribution of external factors such as news, policies, and geolocations. In this paper, we demonstrate the significant relationship between hospitalization rates and COVID-19 infection cases. We then adapt a transfer learning architecture with dynamic location-aware sentiment and semantic analysis (TLSS) to a new application scenario: hospitalization rate prediction during COVID-19. This architecture learns and transfers general transmission patterns of existing epidemic diseases to predict hospitalization rates during COVID-19. We combine the learned knowledge with time series features and news sentiment and semantic features in a dynamic propagation process. We conduct extensive experiments to compare the proposed approach with several state-of-the-art machine learning methods with different lead times of ground truth. Our results show that TLSS exhibits outstanding predictive performance for hospitalization rates. Thus, it provides advanced artificial intelligence (AI) techniques for supporting decision-making in healthcare sustainability.

Suggested Citation

  • Jing Chen & Germán G. Creamer & Yue Ning & Tal Ben-Zvi, 2023. "Healthcare Sustainability: Hospitalization Rate Forecasting with Transfer Learning and Location-Aware News Analysis," Sustainability, MDPI, vol. 15(22), pages 1-24, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:15840-:d:1278018
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    References listed on IDEAS

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    1. Muhammet Gul & Erkan Celik, 2020. "An exhaustive review and analysis on applications of statistical forecasting in hospital emergency departments," Health Systems, Taylor & Francis Journals, vol. 9(4), pages 263-284, October.
    2. Peng Jiang & Jiří Jaromír Klemeš & Yee Van Fan & Xiuju Fu & Yong Mong Bee, 2021. "More Is Not Enough: A Deeper Understanding of the COVID-19 Impacts on Healthcare, Energy and Environment Is Crucial," IJERPH, MDPI, vol. 18(2), pages 1-22, January.
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