Incentivizing Tool-Use Reinforcement Learning for Post-Arrival Emergency Care Prediction
We propose TIR-EC, a tool-integrated reinforcement learning framework that addresses both.
ProofPaper ↗
Key points
- Large language models (LLMs) can reason over both structured measurements and free-text records, which makes them attractive for emergency department (ED) prediction.
- However, most existing LLMs still fall short of conventional ML methods like trees on EHR-based prediction tasks.
- TIR-EC exposes EHR feature groups as tools the LLM can call.
- Evaluation on three clinical prediction tasks shows that our 4B agent achieves the highest macro F1 of all compared models, outperforms conventional ML baselines, and is competitive with much larger frontier LLMs. Further analysis shows that TIR-EC selects more useful evidence with fewer tool calls and combines measurements more effectively.
Sources (1)
- [1]Incentivizing Tool-Use Reinforcement Learning for Post-Arrival Emergency Care PredictionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 07:53 AM
We propose TIR-EC, a tool-integrated reinforcement learning framework that addresses both.
Large language models (LLMs) can reason over both structured measurements and free-text records, which makes them attractive for emergency department (ED) prediction.
Extractive summary: sentences quoted from the sources.
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