A Hybrid Approach for Named Entity Recognition in Clinical Case Reports - Ramos, Fernanda de O.; Marvila, Cauã G.; Colombo, Cristiano da S. (Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI); 2026: Anais do XXII Simpósio Brasileiro de Sistemas de InformaçãoDO - 10.5753/sbsi.2026.248331, 2026)
Context: Natural Language Processing (NLP) has shown significant impact in healthcare by enabling knowledge extraction from unstructured texts. However, in Brazilian Portuguese, there remains a gap in developing models capable of processing clinical case reports, rich sources of crucial information for diagnostics, treatments, and decision-making. Problem: While some models exist (e.g., BioBERTpt), they are scarce and deliver limited performance for Portuguese clinical texts. The lack of accurate entity recognition reduces the ability to transform raw data into structured information that supports clinical decision-making. Solution: This work presents ClinptHyb, a hybrid Named Entity Recognition (NER) model combining a spaCy-based pipeline (ClinptCy, trained from scratch on SemClinBr) with a Large Language Model (Llama-3.3-70b) as reviewer. The hybrid design enhances entity identification and helps detect inconsistencies or omissions in the corpus. IS Theory: Grounded in the Organizational Information Processing Theory (OIPT), the study emphasizes how structuring clinical narratives reduces information uncertainty and increases organizational capacity to process data, thereby improving decision-making in healthcare contexts. Method: Following Design Science Research, the study included corpus preprocessing, stratified dataset splitting, spaCy model training, and prompt-based LLM revision. Evaluation employed Precision, Recall, and F1-score against the SemClinBr gold standard. Summary of Results: ClinptHyb consistently improved over the baseline ClinptCy and outperformed BioBERTpt in 8 of the 10 evaluated categories. The Negation class reached an F1-score of 0.97, a critical result for safe clinical interpretation. The model also revealed annotation inconsistencies, highlighting its potential as a support tool for dataset refinement. Contributions and Impact on the IS Area: This research presents the first hybrid NER approach that combines spaCy with an LLM for Portuguese clinical texts, aligned with IS for data, information, and knowledge management. It contributes to decision support systems, clinical annotation workflows, and the advancement of NLP technologies adapted to the Brazilian healthcare context.