Enhancing Epidemiological Insights with RAG for SIREVA-SUS Reports - Freitas, Christian; Rabonato, Ricardo Trainotti; Berton, Lilian (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2025)
This study introduces a Retrieval-Augmented Generation (RAG) framework designed to extract and generate epidemiological insights from SIREVA-SUS reports, Brazil’s national surveillance system for bacterial pathogens. By integrating dense information retrieval with generative language models, this approach facilitates explainable question answering over extensive, unstructured epidemiological data, aiding healthcare professionals and researchers in detecting trends, outbreaks, and antimicrobial resistance patterns. The evaluation includes a comparison of various large language models (LLMs), such as the multilingual qwen and Portuguese fine-tuned models Sabia, port5, and Bertimbau. Additionally, the architecture examines multiple retrieval strategies, including vector, dense, sparse, hybrid, and reranker methods. Preliminary findings suggest that the system effectively retrieves relevant information with reasonable BERTScore. This work reinforces the importance of language-specific evaluation and opens new directions for deploying RAG systems in public health decision support.