A semantic interoperability model based on NLP for non-structured health data - Mello, Blanda Helena De; Rigo, Sandro José; Costa, Cristiano André da (Anais Estendidos do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS), 2025)
The increasing volume of unstructured clinical data challenges interoperability, especially in decentralized systems like SUS. This study proposes a semantic model integrating NLP, machine learning, and ontologies to extract and standardize clinical notes. Using Named Entity Recognition (NER), lexical normalization, and alignment with HL7 FHIR and openEHR, the model was tested on COVID-19 data from partner hospitals, demonstrating its effectiveness in structuring unstructured data and enabling scalable interoperability.