A Retrieval Augmented System for Cardiological Electronic Health Records.

Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems have significantly advanced data modelling capabilities and improved opportunities for extracting knowledge from vast and heterogeneous biomedical datasets. Recent research has increasingly focused on integrating LLMs with custom-designed RAGs to create systems capable of handling complex biomedical challenges, with a growing demand for more reliable and precise prediction mechanisms in health-related contexts. This study introduces CardioTRAP, an architecture specifically designed to manage biomedical data, with a primary focus on cardiology. The system employs advanced indexing techniques to enable efficient storage and retrieval by integrating deep learning models that generate contextual and clinically relevant insights. By adopting a hybrid approach that combines supervised and unsupervised learning methods, CardioTRAP ensures both high accuracy and scalability, supporting predictive analytics, patient risk stratification, and the discovery of novel biomarkers. Benchmarks and practical applications, evaluated through state-of-the-art metrics, underscore its ability to enhance the identification of critical clinical features. Finally, CardioTRAP demonstrates how the integration of data management and RAG systems can serve as a bridge between biomedical research and clinical practice.