在传染病传播模型中使用大型语言模型,用于公共卫生准备
Kin On Kwok1,2,3, Tom Huynh4, Wan In Wei1
1JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China.
Computational and structural biotechnology journal
|September 17, 2024
概括
这项研究表明,大型语言模型ChatGPT如何帮助公共卫生从业者构建疾病传播模型. 它展示了人工智能在改善传染病流行病学和流行病准备方面的潜力.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 计算生物学 计算生物学
背景情况:
- 像ChatGPT这样的大型语言模型 (LLM) 提供先进的文本和代码生成功能.
- 它们在公共卫生中的应用可以促进合作,加速研究.
- 在疾病传播模型中研究LLM对于传染病流行病学至关重要.
研究的目的:
- 探索ChatGPT在协助公共卫生从业人员进行疾病传播建模方面的作用.
- 展示使用ChatGPT和公共卫生从业人员共同设计数学传输模型.
- 评估LLM在传染病控制策略中的有用性.
主要方法:
- 采用一个案例研究方法来说明协作过程.
- 自然语言对话被用于与ChatGPT进行代代码生成,改进和调试.
- 该模型是为了适应10天的流行数据而开发的,以估计基本生殖数量 (Ro) 和最终的流行病规模.
- 进行了验证和验证流程.
主要成果:
- 聊天GPT成功开发了一个验证的传播模型,复制了流行病曲线.
- 基本生殖数 (Ro) 的估计值为4.19 (95% CI:4.13-4.26).
- 该模型预测60天内最终的流行病规模为98.3%的人口,突出了Poisson分布的最大概率估计与最小平方法相比的优势.
结论:
- 将LLM纳入医学研究加速了模型开发,并减少了卫生从业人员的技术障碍.
- 法律法规民主化了对先进的建模工具的访问.
- 这项技术有可能提高全球流行病的准备能力,特别是在资源有限的环境中.
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