English(EN) LongAgent: History-Guided Agentic Search for Longitudinal Outcome Prediction

LongAgent 使用历史引导搜索进行纵向结果预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员推出 LongAgent，这是一种新颖的基于代理的方法，旨在改进纵向结果的预测，尤其是在复杂的医疗数据集上。该系统自主探索变量、时间窗口和聚合函数的组合，以识别最具预测性的候选者。LongAgent 利用过去的搜索记忆和数值证据来指导其探索，在合成数据上实现了 1.7376 的平均预测 RMSE，并在真实临床数据上的表现与现有方法相当。 AI

影响这种方法可以通过自动化识别相关变量和时间模式的复杂过程来增强医疗保健领域的预测建模。

排序理由该集群包含一篇详细介绍结果预测新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Siyao Wang, Florian Guitton, Shuojie Fu, Guanyu Tao, Kai Sun, Wenjia Bai · 2026-09-15 04:00

LongAgent：历史引导的代理搜索用于纵向结果预测

arXiv:2609.15859v1 Announce Type: new Abstract: Extracting informative representations from longitudinal data that can predict future outcomes remains a critical challenge in medicine. Medical datasets are inherently heterogeneous, consisting of a large number of variables collec…

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LongAgent：历史引导的代理搜索用于纵向结果预测

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