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相关概念视频

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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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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Single Nucleotide Polymorphisms-SNPs01:05

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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Updated: Jul 12, 2025

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涉及机器学习方法的COVID-19研究:一项文献计量研究

Arzu Baygül Eden1, Alev Bakir Kayi2, Mustafa Genco Erdem3

  • 1Koç University, School of Medicine, Department of Biostatistics, Istanbul, Turkey.

Medicine
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概括

本文献分析揭示了2019年新冠病毒病 (COVID-19) 机器学习 (ML) 和人工智能 (AI) 研究的关键趋势. 它强调了领先的作者,机构和国家,推动了对COVID-19的ML应用的进步.

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科学领域:

  • * 专注于计算机科学,工程和科学技术的交集.
  • * 分析生物医学研究中机器学习 (ML) 和人工智能 (AI) 的勃发展领域.

背景情况:

  • *机器学习 (ML) 和人工智能 (AI) 对于2019年新冠肺炎 (COVID-19) 研究越来越重要.
  • *这些技术支持诊断,预后,治疗和公共卫生策略.
  • * 用文献计量分析来评估该领域的学术工作的质量和影响.

研究的目的:

  • *对2019年新冠肺炎疾病 (COVID-19) 研究中的机器学习 (ML) 应用进行文献分析.
  • * 确定在这个研究领域有影响力的作者,机构,国家和出版物.
  • *为研究人员和临床医生提供利用ML进行COVID-19研究的见解.

主要方法:

  • *使用Web of Science (WoS) 进行了全面的文献搜索.
  • *关键词包括"机器学习"",人工智能"和"COVID-19".
  • * 网络可视化使用VOSviewer 1.6.19由两位独立审核员进行分析.

主要成果:

  • * 美国,中国和印度是基于ML的COVID-19研究成果和引用的领先国家.
  • * 陶黄,法迪·图尔曼和伊姆兰·阿什拉夫被认为是非常多产的作家.
  • *主要的研究领域包括计算机科学和工程,哈佛医学院和华中科技大学等机构的重大贡献.

结论:

  • * 图书统计数据为生产性研究人员,国家和高影响力ML的COVID-19ML出版物提供了有价值的见解.
  • * 预计ML和AI建模的进步是由于新数据和方法从大流行中出现的.
  • * 这项研究强调了ML和AI在应对COVID-19中的开拓性作用.