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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records.

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EHRAgent, a large language model (LLM) tool, allows clinicians to directly query electronic health records (EHRs) using natural language. This system autonomously generates and executes code, significantly improving the efficiency of accessing complex patient data.

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Area of Science:

  • Artificial Intelligence
  • Medical Informatics
  • Clinical Data Management

Background:

  • Clinicians face challenges accessing complex patient data from electronic health records (EHRs).
  • Current data retrieval processes rely on data engineers, which is inefficient and time-consuming.
  • There is a need for direct, natural language interaction with EHR systems for clinical decision-making.

Purpose of the Study:

  • To introduce EHRAgent, a large language model (LLM) agent designed for autonomous interaction with EHR systems.
  • To enable clinicians to retrieve complex patient information using natural language queries.
  • To improve the efficiency and accuracy of clinical data extraction from multi-tabular EHR data.

Main Methods:

  • Formulating EHR data retrieval as a multi-tabular reasoning and tool-use planning task.
  • Developing EHRAgent with accumulative domain knowledge and robust coding capabilities for autonomous code generation and execution.
  • Injecting relevant medical information to enhance EHRAgent's reasoning about clinical queries.
  • Integrating interactive coding and execution feedback for iterative code improvement.

Main Results:

  • EHRAgent demonstrated strong performance in tackling complex clinical tasks.
  • The system successfully decomposed complex queries into manageable actions using external toolsets.
  • Experiments on three real-world EHR datasets showed EHRAgent outperformed the strongest baseline by up to 29.6% in success rate.
  • EHRAgent effectively learned from error messages to iteratively improve code generation.

Conclusions:

  • EHRAgent empowers clinicians to directly interact with EHRs using natural language, reducing reliance on data engineers.
  • The LLM agent shows significant potential for improving the efficiency of clinical data retrieval and analysis.
  • EHRAgent's ability to autonomously generate, execute, and refine code makes it a valuable tool for complex clinical tasks.