aceTrajOnco: a multi-agent framework for temporal reasoning over longitudinal EHR for multi-cancer early detection
Abstract Accurate estimation of cancer risk from longitudinal electronic health records (EHRs) could support earlier detection and improved care, but modeling such complex patient trajectories remains challenging. We present TrajOnco, a training-free, multi-agent large language model (LLM) framework designed for scalable multi-cancer early detection. Using a chain-of-agents architecture with long-term memory, TrajOnco performs temporal reasoning over sequential clinical events to generate patient-level summaries…
A training-free multi-agent LLM system predicted 1-year cancer diagnosis risk from longitudinal health records, reaching AUROCs of 0.64–0.80 and matching supervised machine learning in a lung cancer benchmark.
The evaluation used matched case-control cohorts, and performance varied across cancer types with AUROCs from 0.64 to 0.80, so prospective screening performance remains untested.
Evidence
- Peer-reviewednpj Digital Medicine2026-10-15
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Truvace Impact Record TRV-2026-1596, v1: “TrajOnco: a multi-agent framework for temporal reasoning over longitudinal EHR for multi-cancer early detection.” Truvace, 2026-10-16. /record/TRV-2026-1596 (accessed at citation time). sha256 c56219a9809d9e24…
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