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适应性大语言模型用于疫苗预测:一种新的方法用于用工程偏差提示预测疫苗需求

Mingming Chen^1,2, Qiangsong Wu³, Zilong Wang⁴

¹Academy of Pharmacy, Xi'an Jiaotong-Liverpool University, Ren'ai Road, Suzhou, Jiangsu, People's Republic of China.

PLOS digital health

|March 9, 2026

概括

准确的疫苗需求预测是改进了一个新的适应性大型语言模型的疫苗预测 (ALLMVP). 这种人工智能模型提高了预测准确性,最大限度地减少浪费和优化免疫计划.

科学领域:

人工智能的人工智能
公共卫生公共卫生
流行病学流行病学

背景情况:

准确的疫苗需求预测对于有效的免疫计划和减少浪费至关重要.
传统的预测模型往往难以处理复杂的数据模式并达到高准确度.

研究的目的:

引入和评估用于疫苗预测的适应性大语言模型 (ALLMVP).
将ALLMVP的性能与用于疫苗需求预测的传统机器学习模型进行比较.

主要方法:

开发了ALLMVP,将大型语言模型 (LLM) 架构与自适应性值校正机制集成在一起.
利用上海徐区 (2014-2022) 的疫苗接种记录数据进行比较分析.
将ALLMVP与后勤回归,随机森林,长短期记忆及其增强版本进行比较.

主要成果:

基于LLM的框架,包括ALLMVP,与传统模型相比,显著提高了预测能力.
ALLMVP获得了71.43%的优异接受率.
即使在COVID后的时代,ALLMVP在估计对七种国家免疫计划疫苗的需求方面表现出一致的准确性.

结论:

适应性LLM驱动的预测工具可以满足严格的预测准确性标准.
ALLMVP显示了基于数据的疫苗接种策略和公共卫生资源管理的潜力.
ALLMVP 的人工智能基础设施可用于更广泛的预测应用.

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