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预测COVID-19患者的急诊室重访使用多源传输学习的预测

Yuelyu Ji1, Yuhe Gao2, Runxue Bao3

  • 1Department of Information Science, School of Computing and Information, University of Pittsburgh, Pittsburgh,USA.

Proceedings. IEEE International Conference on Healthcare Informatics
|March 15, 2024
PubMed
概括

识别可能再次访问急诊室 (ER) 的COVID-19患者至关重要. 一个新的深度转移学习模型,Multi-DANN,有效地预测了使用电子健康记录 (EHR) 的7天ER重访.

关键词:
在 COVID-19 疫情中,深度转移学习 (deep transfer learning) 是一种深度转移学习.域对抗性神经网络的领域.在急诊室重新检查.多个来源多个来源.

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Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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