EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records

arXiv:2401.07128v2 Announce Type: replace
Abstract: Large language models (LLMs) have demonstrated exceptional capabilities in planning and tool utilization as autonomous agents, but few have been developed for medical problem-solving. We propose EHRAgent, an LLM agent empowered with a code interface, to autonomously generate and execute code for multi-tabular reasoning within electronic health records (EHRs). First, we formulate an EHR question-answering task into a tool-use planning process, efficiently decomposing a complicated task into a sequence of manageable actions. By integrating interactive …

abstract agent agents arxiv autonomous autonomous agents capabilities code cs.ai cs.cl electronic electronic health records few-shot generate health language language models large language large language models llm llms medical planning problem-solving reasoning records tabular tool type