Knowledge-Infused LLM-Powered Conversational Health Agent: A Case Study for Diabetes Patients
This addresses the need for more accurate and domain-specific health advice for diabetes patients, though it is incremental as it builds on existing frameworks and methods.
The paper tackles the problem of inaccurate responses in LLM-based diabetes management by proposing a knowledge-infused conversational health agent that integrates domain-specific dietary guidelines and analytical tools, showing superior performance in generating responses for managing essential nutrients compared to GPT4 in an evaluation with 100 diabetes-related questions.
Effective diabetes management is crucial for maintaining health in diabetic patients. Large Language Models (LLMs) have opened new avenues for diabetes management, facilitating their efficacy. However, current LLM-based approaches are limited by their dependence on general sources and lack of integration with domain-specific knowledge, leading to inaccurate responses. In this paper, we propose a knowledge-infused LLM-powered conversational health agent (CHA) for diabetic patients. We customize and leverage the open-source openCHA framework, enhancing our CHA with external knowledge and analytical capabilities. This integration involves two key components: 1) incorporating the American Diabetes Association dietary guidelines and the Nutritionix information and 2) deploying analytical tools that enable nutritional intake calculation and comparison with the guidelines. We compare the proposed CHA with GPT4. Our evaluation includes 100 diabetes-related questions on daily meal choices and assessing the potential risks associated with the suggested diet. Our findings show that the proposed agent demonstrates superior performance in generating responses to manage essential nutrients.