发表机构
Imperial College London（伦敦帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
LongAgent 是一种基于智能体的方法，通过历史记忆引导搜索变量组合、时间窗口和聚合函数，在合成数据上提升预测精度，在临床数据上与最佳基线相当。
AI 中文摘要
从纵向数据中提取能够预测未来结局的信息性表示，仍然是医学领域的一项关键挑战。医学数据集本质上是异质的，包含来自不同来源的大量变量，这些变量以不同的时间间隔采样，并代表人类健康状况的不同方面。这要求识别具有预测价值的变量、处理纵向信息，并整合多个变量以进行结局预测。在此，我们提出了一种新颖的基于智能体的方法 LongAgent，它能够自主搜索变量集、时间窗口和纵向聚合函数的组合，并识别具有良好预测性能的候选方案。LongAgent 利用先前搜索的历史记忆和数值证据来指导后续探索。在合成数据上，LongAgent 实现了平均预测 RMSE 为 1.7376，比最强的非智能体基线提高了 0.0151（95% 置信区间：[0.0045, 0.0260]；p=0.0273）。在真实临床数据集上，其表现与最佳基线相当。
英文摘要
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 collected from different sources, sampled with different temporal spacings, and representing different aspects of human health status. This requires identifying those variables with predictive value, processing longitudinal information, and integrating multiple variables for outcome prediction. Here, we propose a novel agent-based approach, LongAgent, that can autonomously search over combinations of variable sets, temporal windows and longitudinal aggregation functions, and identify candidates with promising predictive performance. LongAgent utilises a history memory of previous searches and numerical evidence to guide subsequent exploration. On synthetic data, LongAgent achieves a mean prediction RMSE of 1.7376 and improves over the strongest non-agent baseline by 0.0151 (95% CI: [0.0045,0.0260]; p=0.0273). On a real clinical dataset, it performs comparably to the best baseline.
CommentsThis paper is accepted to the MICCAI 2026 Agentic AI for Medicine Workshop
