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

arXiv: https://arxiv.org/abs/2504.16448 · HTML: https://mlsanity.com/paper/2504.16448/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 23 Apr 2025.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records](https://mlsanity.com/paper/2203.03540/) (2022)
- [Using an LLM to Help With Code Understanding](https://mlsanity.com/paper/2307.08177/) (2023)
- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2309.14717/) (2023)
- [CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors](https://mlsanity.com/paper/2305.05711/) (2023)
- [Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis](https://mlsanity.com/paper/2309.12368/) (2023)
- [CodeKGC: Code Language Model for Generative Knowledge Graph Construction](https://mlsanity.com/paper/2304.09048/) (2023)
- [C-ICL: Contrastive In-context Learning for Information Extraction](https://mlsanity.com/paper/2402.11254/) (2024)
- [Code-Style In-Context Learning for Knowledge-Based Question Answering](https://mlsanity.com/paper/2309.04695/) (2023)
- [CoCoP: Enhancing Text Classification with LLM through Code Completion Prompt](https://mlsanity.com/paper/2411.08979/) (2024)

## Built on by (most cited first, 1 of 1)

- [From Analytics to Tumor Boards: An Evidence-Linked Multi-Agent Workflow for Oncology Feature Extraction](https://mlsanity.com/paper/2608.28974/) (2026)

## Abstract (arXiv)

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.