使用机器学习技术 (早期人工智能支持的响应与社交倾听平台) 来增强COVID-19传染病的数字社会理解:开发和实施研究
Becky K White1, Arnault Gombert2, Tim Nguyen1
1Department of Epidemic and Pandemic Preparedness and Prevention, World Health Organization, Geneva, Switzerland.
JMIR infodemiology
|July 9, 2023
概括
世界卫生组织 世界卫生组织
科学领域:
- 公共卫生信息学 公共卫生信息学
- 计算社会科学 计算社会科学
- 流行病学 流行病学
背景情况:
- 随着COVID-19的流行,人们需要快速的社会理解来管理传染病.
- 传统的社交媒体分析平台,设计用于商业用途,在公共卫生洞察方面未得到充分利用.
- 需要新的工具来克服应用社会倾听到公共卫生监测中的挑战.
研究的目的:
- 描述世界卫生组织早期人工智能支持响应与社会倾听 (EARS) 平台的发展.
- 详细介绍数据采集,机器学习模型开发和验证过程.
- 介绍EARS平台试点研究的发现.
主要方法:
- 每日收集9种语言的基于网络的对话.
- 开发一个公共卫生分类法,其中包括5个主要类别和41个子类别,用于COVID-19叙述.
- 应用半监督机器学习算法并与布尔搜索过器进行验证 (测量回忆和精度) 的比较.
主要成果:
- EARS平台已成功开发,验证并用于分析2020年12月至2022年2月的COVID-19对话.
- 与英语和西班牙语的布尔搜索过器 (P<.001) 相比,机器学习算法显示出更高的精度和回忆能力.
- 人口统计过器提供了有价值的见解,用户性别分布与一般社交媒体使用模式保持一致.
结论:
- EARS平台有效地满足公共卫生分析师在COVID-19流行病等健康危机期间的需求.
- 将公共卫生分类与人工智能集成到一个用户友好的社交倾听工具中,可以增强对全球叙事的理解.
- 机器学习提供了一种比基于关键字的方法更准确和可扩展的方法,用于分析信息流行期间的数字社会数据.
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