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COVID-19 has a huge impact on the medical system. In this paper, we collected all hospitalization data from Zhanjiang and four machine learning models are compared to find the most suitable model for this dataset. Our method use interpolation to upsample the time series, then predicts and calculates the error, and finally clusters the prediction results to find out the diseases greatly affected by COVID-19. Through the influencing factors of hospitalization decision-making, we find that why these diseases were affected by the COVID-19 epidemic. Finally, combined with relevant medical theories, we analyze some problems that may exist in this background, and provides countermeasures and suggestions for the regulatory authorities, so as to better save medical resources and ensure the effectiveness and fairness of medical services.

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Analysis of Hospitalization Data Before, During and After the COVID-19 Epidemic Using Short Time Series Clustering

  • 会议论文

  • 发表日期：

    2025年

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  • Zequn Guan,
  • Chao Che,
  • Lizhong Liang

摘要

COVID-19 has a huge impact on the medical system. In this paper, we collected all hospitalization data from Zhanjiang and four machine learning models are compared to find the most suitable model for this dataset. Our method use interpolation to upsample the time series, then predicts and calculates the error, and finally clusters the prediction results to find out the diseases greatly affected by COVID-19. Through the influencing factors of hospitalization decision-making, we find that why these diseases were affected by the COVID-19 epidemic. Finally, combined with relevant medical theories, we analyze some problems that may exist in this background, and provides countermeasures and suggestions for the regulatory authorities, so as to better save medical resources and ensure the effectiveness and fairness of medical services.