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arXiv AI By Pengfei Hu, Chenwei Wu, Qiao Jin, Jun Deng, Zhiyong Lu, Liyue Shen, Yue Ning

Incentivizing Tool-Use Reinforcement Learning for Post-Arrival Emergency Care Prediction

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The paper introduces TIR-EC, a tool‑integrated reinforcement learning framework designed to improve large language models (LLMs) for emergency department prediction tasks. TIR-EC exposes groups of electronic health record (EHR) features as callable tools and trains the LLM using rewards from a gradient‑boosted tree teacher, while a reverse curriculum gradually removes prompt‑based skills to embed task‑specific clinical knowledge into the model weights. Experiments on three clinical prediction tasks show that a 4B TIR‑EC agent achieves the highest macro F1 among compared models, surpasses conventional machine‑learning baselines, and competes with much larger frontier LLMs, while also selecting more useful evidence with fewer tool calls.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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