RAG-Based Blood Test Report Analysis to Reverse Metabolic Syndrome

Abstract

Metabolic Syndrome is a group of health conditions which together enhance the risk of getting heart disease, type 2 diabetes, and stroke. These include increased blood glucose, blood pressure, increased body fat, abnormal triglyceride to HDL ratio, abnormal red blood cell width distribution, increased glycated hemoglobin, and insulin resistance, among others. These health metrics are essential indicators of the wellness of a person. Still, interpretation of such complex medical indicators in a blood test report prove difficult for the general population. This research paper aims to simplify complex medical terminologies associated with Metabolic Syndrome, identify the most relevant blood test parameters, and generate tailored lifestyle and dietary suggestions. To achieve this, we propose a Generative Artificial Intelligence (GenAI)-based system that automates the interpretation of blood test data and provides users with personalized health insights in clear and accessible language. Our implementation utilizes the Retrieval Augmented Generation (RAG) framework built on top of the LLaMA3 base model. The RAG architecture allows the system to retrieve accurate and relevant information from a curated knowledge base composed of medical literature, academic books, YouTube videos, and research papers focused on Metabolic Syndrome. Based on the analyzed data, the system not only offers actionable recommendations but also alerts users when medical consultation may be required. This approach empowers patients with actionable insights to improve their health and well-being based on their blood test data.