EMRModel: A Large Language Model for Extracting Medical Consultation Dialogues into Structured Medical Records
Journal:
arXiv
Published Date:
Apr 23, 2025
Abstract
Medical consultation dialogues contain critical clinical information, yet
their unstructured nature hinders effective utilization in diagnosis and
treatment. Traditional methods, relying on rule-based or shallow machine
learning techniques, struggle to capture deep and implicit semantics. Recently,
large pre-trained language models and Low-Rank Adaptation (LoRA), a lightweight
fine-tuning method, have shown promise for structured information extraction.
We propose EMRModel, a novel approach that integrates LoRA-based fine-tuning
with code-style prompt design, aiming to efficiently convert medical
consultation dialogues into structured electronic medical records (EMRs).
Additionally, we construct a high-quality, realistically grounded dataset of
medical consultation dialogues with detailed annotations. Furthermore, we
introduce a fine-grained evaluation benchmark for medical consultation
information extraction and provide a systematic evaluation methodology,
advancing the optimization of medical natural language processing (NLP) models.
Experimental results show EMRModel achieves an F1 score of 88.1%, improving
by49.5% over standard pre-trained models. Compared to traditional LoRA
fine-tuning methods, our model shows superior performance, highlighting its
effectiveness in structured medical record extraction tasks.
