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人工智能 (AI) 方法,特别是自然语言处理 (NLP),可以有效地从社交媒体和数据库中获取公共卫生见解. 这种方法加强了相关和适用的健康指南的制定,特别是在流行病等快速反应场景中.

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科学领域:

  • 公共卫生 公共卫生
  • 医疗信息学 医疗信息学
  • 人工智能的人工智能

背景情况:

  • 卫生专业人员,公民和患者的经验知识对于制定相关的公共卫生和临床指南至关重要.
  • 结合这些知识的传统方法耗时且涉及有限的代表性.
  • 快速准则制作,特别是在流行病期间,在整合经验洞察力方面面临挑战.

研究的目的:

  • 探索基于人工智能 (AI) 的方法的潜力,以捕捉体验知识和价值考虑.
  • 为制定公共卫生指南提供这些见解.
  • 解决指导方针制定中的传统方法的局限性.

主要方法:

  • 开发和检查基于人工智能的方法,用于荷兰COVID-19疫苗接种指南的制定.
  • 对荷兰社交媒体信息和国家公共卫生与环境研究所 (RIVM) 数据库的分析 (2020年12月 - 2021年6月).
  • 应用自然语言处理 (NLP) 过和监督机器学习进行数据分析,然后进行结构主题建模.

主要成果:

  • 在大型数据集中,NLP方法成功地识别和分析了基于经验的知识和价值考虑.
  • 获得了对各种经验知识的洞察,这些知识对于快速制定指南至关重要.
  • 确定的主题突出了指导方针建议与现实需求之间的差异,例如对必要旅行的疫苗优先级和定义风险组的模糊性.

结论:

  • NLP方法是获取和利用基于经验的知识的可行工具,有助于制定强大和适用的指南.
  • 在传统方法难以实施的情况下,基于人工智能的方法提供了有价值的替代方案.
  • 这些人工智能工具可以扩大证据基础,以便快速制定指南,提高疫情防控能力.