TrajOnco uses a chain-of-agents LLM architecture with memory to perform temporal reasoning on longitudinal EHR, achieving 0.64-0.80 AUROC for 1-year multi-cancer risk prediction in zero-shot mode on matched cohorts while matching supervised ML on lung cancer and outperforming single-agent baselines.
InProceedings of the 27th International Conference on Computational Linguistics, pages 3402–3413, Santa Fe, New Mexico, USA
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
LiFT instruction fine-tunes LLMs with a temporal curriculum to improve in-context learning on longitudinal NLP tasks, yielding gains on out-of-distribution data and rare change events across multiple model sizes.
citing papers explorer
-
TrajOnco: a multi-agent framework for temporal reasoning over longitudinal EHR for multi-cancer early detection
TrajOnco uses a chain-of-agents LLM architecture with memory to perform temporal reasoning on longitudinal EHR, achieving 0.64-0.80 AUROC for 1-year multi-cancer risk prediction in zero-shot mode on matched cohorts while matching supervised ML on lung cancer and outperforming single-agent baselines.
-
LiFT: Does Instruction Fine-Tuning Improve In-Context Learning for Longitudinal Modelling by Large Language Models?
LiFT instruction fine-tunes LLMs with a temporal curriculum to improve in-context learning on longitudinal NLP tasks, yielding gains on out-of-distribution data and rare change events across multiple model sizes.