Assessing Large Language Models for Structuring Patient Records - Campos, Eduardo M.; Conrado, Rafael C. G.; Traina Jr., Caetano; Traina, Agma J. M.; Cazzolato, Mirela T. (Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD); 2025: Anais Estendidos do XL Simpósio Brasileiro de Bancos de Dados, 2025)
Patient Records are digital documentation of a person’s presence in a health institution. This data has been used to enhance medical experience and has allowed for many breakthroughs in scientific research. Nevertheless, the unstructured and non-standard nature of medical notes constitutes an obstacle to the complete usability of this information in crucial tools such as data analysis, visualization, and machine learning algorithms. In this work, we evaluated the use of Large Language Models to preprocess the unlabeled data into a defined structure, test different model sizes and architectures, and develop prompt strategies and fine-tuning techniques. The proposal was evaluated using the Covid-19 DataSharing dataset. Even low-computation cost models can achieve great performance, which could enable an approach to standardize many kinds of data.