Copd-ChatGLM: A Chronic Obstructive Pulmonary Disease Diagnostic Model

COPD is a chronic lung condition characterized by persistent respiratory obstruction and airflow limitation. Early detection and diagnosis are crucial to manage the disease effectively and improve patient outcomes. However, many patients, especially in low- and middle-income countries, are diagnosed late due to limited access to spirometry. This study proposes Copd-ChatGLM, a large language model fine-tuned for COPD diagnosis and management using the RAG framework. The model combines deep learning with clinical data to enhance diagnostic accuracy and provide personalized treatment plans. By integrating LoRA, Copd-ChatGLM fine-tunes the ChatGLM3-6B model efficiently to perform COPD-related tasks with minimal computational resources. Experimental results show that Copd-ChatGLM outperforms traditional classification models and general large language models in accuracy, sensitivity, specificity, and F1 score. This model has become a robust and clinically applicable tool for managing COPD, particularly in resource-limited settings.