EMRModel: A Large Language Model for Extracting Medical Consultation Dialogues into Structured Medical Records (2504.16448v1)

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 LLMs 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 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.

• The paper introduces EMRModel, a large language model employing LoRA and code-style prompts to extract structured records from medical consultation dialogues.
• Experimental results show EMRModel achieved a remarkable 88.1% F1 score, representing a 49.5% improvement over standard models.
• This approach offers a scalable and efficient solution for transforming unstructured medical dialogues into usable data for advanced healthcare analytics and AI applications

EMRModel: A LLM for Extracting Medical Consultation Dialogues into Structured Medical Records

This paper introduces EMRModel, an advanced approach for transforming unstructured medical consultation dialogues into structured electronic medical records (EMRs). The authors address a pressing issue in medical informatics: the challenge of extracting essential clinical information from unstructured doctor-patient dialogues to aid diagnosis and treatment. Conventional strategies have often relied on labor-intensive manual entry and rule-based or shallow machine learning techniques, which fail to capture the deep semantic structures necessary for robust data integration and analysis. The emergence of LLMs and techniques like Low-Rank Adaptation (LoRA) present new opportunities to overcome these limitations.

Methodology and Implementation

The proposed EMRModel leverages a combination of LoRA-based fine-tuning and a novel code-style prompt design. The model aims to efficiently convert medical consultation dialogues into structured EMRs by encoding text into a coder-style format that these models can more accurately parse. The LoRA technique allows for lightweight fine-tuning by focusing on low-rank matrices, which optimizes performance without exhaustive computational demands.

Key to the model's success is the use of a high-quality dataset comprising over 8,000 realistically grounded medical consultation records with detailed annotations. This data-driven approach ensures EMRModel is finely attuned to the nuanced language of medical dialogues across various departments and specialties.

Evaluation and Results

The experimental results demonstrate that EMRModel achieves a remarkable F1 score of 88.1%, representing a 49.5% improvement over standard pre-trained models. Such performance underscores the efficacy of the approach in accurately extracting structured records from complex dialogues. Detailed examination reveals that EMRModel, when coupled with coder-style prompts, significantly enhances extraction accuracy compared to traditional natural language prompts.

Implications and Future Directions

The implications of this research extend beyond mere record generation, touching upon broader themes in AI-driven healthcare diagnostics, intelligent patient management systems, and personalized treatment planning. By converting linguistic data into structured records, the methodology aids in improving data usability for advanced analytics and insights generation.

Future research could explore integrating EMRModel with medical knowledge bases to further enhance the semantic understanding of complex medical scenarios. Additionally, deploying this model across different institutional settings may provide insights into its adaptability and robustness. The computational efficiency offered by LoRA makes this approach scalable, presenting avenues for extensive deployment across various healthcare applications.

Conclusion

Overall, the EMRModel provides a sophisticated and effective solution to the long-standing challenge of processing unstructured medical dialogues. The innovative use of code-style prompts combined with LoRA fine-tuning sets a new standard in the domain of medical information extraction, offering evidence of significant gains in both accuracy and operational efficiency. This work represents an important step towards more intelligent and streamlined health informatics systems.