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LongAgent: History-Guided Agentic Search for Longitudinal Outcome Prediction

Paper recorded by Signals 4 on 2026-09-14 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-14 on arXiv · recorded by Signals 4 on 2026-09-15

Category: cs.AI · 人工智能 · first seen 2026-09-15

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 collected from different sources, sampled with different temporal spacings, and representing different aspects of human health status. This requires identifying those variabl

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#72 most recent of 300 cs.AI papers we have recorded · ↑ newer: LLM-Based Schema-Aware Split Learning for Privacy-Preserving Mental Di · ↓ older: K-Bench: a clinically calibrated benchmark for evaluating large langua
Cite this page: LongAgent: History-Guided Agentic Search for Longitudinal Outcome Prediction: the #72 most recent of 300 cs.AI papers we have recorded (as of 2026-09-14). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/longagent-history-guided-agentic-search-for-longitudinal-outcome-prediction.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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