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English(EN) Auditable Context-Aware HFMD Forecasting with Structured LLM Agents

LLM代理通过可审计、上下文感知的预测增强手足口病预测

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新研究论文介绍了一个双代理神经符号框架，旨在实现对手足口病（HFMD）更具可审计性和上下文感知的预测。该系统集成了基于LLM的事件解释器，用于处理学校日程和政府报告等各种信号，生成传播影响信号。然后，该信号被预测生成器使用，该生成器将其与历史数据相结合，以产生概率预测和简洁的理由，在准确性和可解释性方面优于传统模型和基础模型。 AI

影响 该框架展示了LLM代理如何提高公共卫生应用中时间序列预测的可解释性和准确性。

排序理由 研究论文，详细介绍了用于疾病预测的新型神经符号框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

LLM代理通过可审计、上下文感知的预测增强手足口病预测

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研究论文，详细介绍了用于疾病预测的新型神经符号框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Joongwon Chae, Runming Wang, Chen Xiong, Gong Yunhan, Lian Zhang, Ji Jiansong, Dongmei Yu, Peiwu Qin ·

    具有结构化LLM代理的可审计上下文感知HFMD预测

    arXiv:2511.23276v2 Announce Type: replace Abstract: Effective HFMD surveillance requires forecasts capturing both time-series patterns and contextual drivers such as school calendars, weather, and policy or surveillance reports. In clinical settings, forecasts must be trusted and…

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