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基于机器学习的方法来识别研究差距:COVID-19作为一个案例研究.

Alaa Abd-Alrazaq1, Abdulqadir J Nashwan2, Zubair Shah3

  • 1AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.

JMIR formative research
|March 5, 2024
PubMed
概括

本研究引入了一种机器学习方法,以识别科学文献中的研究差距,使用COVID-19研究作为案例研究. 该方法有效地确定了未来科学探索的关键领域.

关键词:
贝尔特 (BERT) 公司贝尔主题 贝尔主题在这里,我们可以看到COVID COVID COVID.在 COVID-19 疫情中,在NLP中,我们使用了NLP.这就是SARS-CoV-2病毒.冠状病毒冠状病毒病毒文献审查 文献审查机器学习是机器学习.自然语言处理自然语言处理.研究差距研究差距研究的差距研究的差距研究主题研究主题研究主题研究主题研究主题审查方法论 审查方法论审查方法 审查方法.科学文献科学文献文本分析 文本分析主题聚类 主题聚类.

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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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