Exploring the Potential of Large Language Models in Personalized Diabetes Treatment Strategies

Abstract

Abstract Object: This study aims to explore the application of a fine-tuned model-based outpatient treatment support system for the treatment of patients with diabetes and evaluate its effectiveness and potential value. Methods: The ChatGLM model was selected as the subject of investigation and trained using the P-tuning and LoRA fine-tuning methods. Subsequently, the fine-tuned model was successfully integrated into the Hospital Information System (HIS). The system generates personalized treatment recommendations, laboratory test suggestions, and medication prompts based on patients' basic information, chief complaints, medical history, and diagnosis data. Results: Experimental testing revealed that the fine-tuned ChatGLM model is capable of generating accurate treatment recommendations based on patient information, while providing appropriate laboratory test suggestions and medication prompts. However, for patients with complex medical records, the model's outputs may carry certain risks and cannot fully substitute outpatient physicians' clinical judgment and decision-making abilities. The model's input data is confined to electronic health record (EHR), limiting the ability to comprehensively reconstruct the patient's treatment process and occasionally leading to misjudgments of the patient's treatment goals. Conclusion: This study demonstrates the potential of the fine-tuned ChatGLM model in assisting the treatment of patients with diabetes, providing reference recommendations to healthcare professionals to enhance work efficiency and quality. However, further improvements and optimizations are still required, particularly regarding medication therapy and the model's adaptability.