EMRModel: A Large Language Model for Extracting Medical Consultation Dialogues into Structured Medical Records

• URL: http://arxiv.org/abs/2504.16448v1
• Date: Wed, 23 Apr 2025 06:17:55 GMT
• Title: EMRModel: A Large Language Model for Extracting Medical Consultation Dialogues into Structured Medical Records
• Authors: Shuguang Zhao, Qiangzhong Feng, Zhiyang He, Peipei Sun, Yingying Wang, Xiaodong Tao, Xiaoliang Lu, Mei Cheng, Xinyue Wu, Yanyan Wang, Wei Liang,
• Abstract summary: We propose EMRModel, a novel approach that integrates LoRA-based fine-tuning with code-style prompt design.<n>We construct a high-quality, realistically grounded dataset of medical consultation dialogues with detailed annotations.<n> Experimental results show EMRModel achieves an F1 score of 88.1%, improving by49.5% over standard pre-trained models.
• Score: 11.013242961199204
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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