LongAgent: History-Guided Agentic Search for Longitudinal Outcome Prediction
LongAgent autonomously searches variable sets and temporal windows to predict longitudinal medical outcomes, beating the strongest non-agent baseline on synthetic data.
The paper proposes LongAgent, an agent-based method that searches over combinations of variable sets, temporal windows and aggregation functions for outcome prediction on heterogeneous medical longitudinal data. It uses a history memory of previous searches and numerical evidence to guide exploration. On synthetic data it achieves mean RMSE 1.7376, improving over the best non-agent baseline by 0.0151 (95% CI [0.0045, 0.0260]; p=0.0273), and performs comparably to the best baseline on a real clinical dataset.
- Agent autonomously searches variable sets, temporal windows and aggregation functions
- History memory of prior searches and numerical evidence guides exploration
- RMSE 1.7376 on synthetic data, improvement of 0.0151 over best baseline
- Matches best baseline on real clinical dataset
From the original article
1 min read at arxiv.orgExtracting 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.
By Siyao Wang · arxiv.org
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