Key result
Transfer learning via location-aware news analysis outperforms standard methods for predicting COVID-19 hospitalization rates.
The proposed TLSS architecture provides an advanced AI technique for accurately forecasting hospitalization rates during emerging epidemic diseases like COVID-19.
Supports AI-driven epidemic forecasting tools; leaves open prospective clinical validation before practice adoption.
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.
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Chen et al. (2023) studied COVID-19. Transfer learning architecture with dynamic location-aware sentiment and semantic analysis (TLSS) vs. State-of-the-art machine learning methods was evaluated on Hospitalization rate prediction. A transfer learning architecture (TLSS) using location-aware news analysis showed outstanding predictive performance for COVID-19 hospitalization rates compared to state-of-the-art methods.
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