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Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
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通过使用大型语言模型推进实时传染病预测.

Hongru Du1,2, Yang Zhao1,2, Jianan Zhao3,4

  • 1Center for Systems Science and Engineering, Johns Hopkins University, Baltimore, MD, USA.

Nature computational science
|June 6, 2025
PubMed
概括
此摘要是机器生成的。

流行病LLM使用人工智能预测疾病的传播,通过分析复杂的数据,如公共卫生政策和基因组监测. 这种新的方法改善了实时爆发预测.

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科学领域:

  • 流行病学 流行病学
  • 人工智能的人工智能
  • 计算生物学 计算生物学

背景情况:

  • 由于多模式变量,公共政策和人类行为,预测短期疾病传播是复杂的.
  • 现有的模型很难有效地整合多样化的实时数据.

研究的目的:

  • 介绍PandemicLLM,一个使用多模式大语言模型 (LLM) 进行实时疾病传播预测的框架.
  • 重构疾病传播预测作为一个文本推理问题,以纳入非数字数据.

主要方法:

  • 开发了一个人工智能-人类合作提示设计和时间序列表示学习来编码LLMs的多模式数据.
  • 利用文本公共卫生政策,基因组监测和流行病学时间序列数据.
  • 使用COVID-19数据,在美国所有50个州应用和测试了该模型19个月.

主要成果:

  • 流行病LLM成功地结合了与流行病相关的异质数据格式.
  • 与现有的疾病传播预测模型相比,已证明的性能优势.
  • 通过将疾病传播视为文本推理问题来实现实时预测.

结论:

  • 流行病LLM为在疾病爆发预测中整合各种数据类型提供了一个新的框架.
  • 这种方法有望提高公共卫生预测的准确性和及时性.
  • 为人工智能驱动的复杂,现实世界的流行病学挑战的分析开辟了新的途径.