Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·11d ago·5 min read
EHR-RobustGym: Benchmarking and Training Agents for Robust Clinical Reasoning
TL;DR
EHR-RobustGym is a new benchmarking environment designed to evaluate and train clinical agents using noisy electronic health records (EHRs). It reveals significant performance drops in large language models when faced with data discrepancies, highlighting the need for improved robustness in clinical reasoning.
✦ Why It Matters
Start using EHR-RobustGym to benchmark and enhance the robustness of your clinical AI models today.
Key Takeaways
- Utilize EHR-RobustGym for evaluating clinical AI models against noisy data.
- Implement supervised fine-tuning to improve model performance on clinical tasks.
- Explore the impact of data discrepancies on model outputs.
- Generalize findings from EHR-RobustGym to enhance external EHR applications.
How It Works
EHR-RobustGym simulates real-world clinical scenarios by providing Clean-Noise pairs that challenge agents to respond accurately despite data inconsistencies. It allows for interactive SQL/Python execution, enabling users to collect trajectories and verify outcomes, which aids in training more robust models.