Summary of Ehragent: Code Empowers Large Language Models For Few-shot Complex Tabular Reasoning on Electronic Health Records, by Wenqi Shi et al.
EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records
by Wenqi Shi, Ran Xu, Yuchen Zhuang, Yue Yu, Jieyu Zhang, Hang Wu, Yuanda Zhu, Joyce Ho, Carl Yang, May D. Wang
First submitted to arxiv on: 13 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to medical problem-solving using large language models (LLMs) as autonomous agents. The authors introduce EHRAgent, an LLM agent empowered with a code interface that can autonomously generate and execute code for multi-tabular reasoning within electronic health records (EHRs). By formulating EHR question-answering tasks into tool-use planning processes and integrating interactive coding and execution feedback, EHRAgent learns from error messages and improves its generated code through iterations. Additionally, the authors enhance the LLM agent with long-term memory, allowing it to effectively select and build upon successful cases from past experiences. Experimental results on three real-world datasets show that EHRAgent outperforms the strongest baseline by up to 29.6% in success rate. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to help doctors solve medical problems. It proposes a new way for computers to understand and work with electronic health records, which are like digital files of patient information. The computer system, called EHRAgent, can figure out what kind of code it needs to write to answer specific questions about patients’ health. It does this by breaking down the problem into smaller steps and trying different solutions until it gets the right one. EHRAgent also remembers what worked well in the past and uses that information to improve its answers. |
Keywords
» Artificial intelligence » Question answering
