动态洞察力:通过综合语言模型和时空分析,揭开公共需求在卫生紧急情况中的演变
Yuan Zhang1, Lin Fu2, Xingyu Guo1
1School of Management, Capital Normal University, Beijing, People's Republic of China.
Risk management and healthcare policy
|October 23, 2024
概括
在卫生危机期间分析公众需求至关重要. 本研究引入了使用大型语言模型 (LLM) 的时空分析方法,以快速评估社会媒体数据的公共需求,以更好地管理紧急情况.
科学领域:
- 公共卫生 公共卫生
- 应急管理 应急管理
- 数据科学数据科学数据科学
背景情况:
- 在公共卫生紧急情况中,有效的危机沟通取决于了解公众的需求.
- 公共需求提供了对健康危机不断演变的状态的本能见解.
- 政府必须利用公众需求信息来有效管理卫生紧急情况.
研究的目的:
- 开发和验证一个空间时间演变分析方法,用于紧急情况下的公共需求.
- 通过分析公共需求,提高卫生紧急情况管理的有效性和效率.
主要方法:
- 使用了动态主题挖掘和知识图表构建.
- 开发了一种针对紧急情况的新型时空演变分析方法,利用三个大型语言模型 (LLM):ERNIE,BERTopic和UIE (EBU模型).
主要成果:
- 该方法成功地使用社交媒体数据分析了上海COVID-19封锁期间的公众需求.
- 它在标签和培训方面表现出效率,使公众需求的快速,全面和准确的时空分析成为可能.
- 视觉分析确定了公共需求的地理热点,为政策和资源分配提供了信息.
结论:
- 这项研究扩大了研究医疗紧急情况管理的范围和方法.
- 社交媒体数据是分析公众需求的宝贵资源.
- 拟议的方法在紧急情况下为各种公共服务部门提供快速决策支持.
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