Adaptive Clinical Vocabulary Expansion with Biomedical LLMs in Electronic Health Records

• Nadine Anderle PUCRS
• Dalvan Griebler PUCRS

DOI: https://doi.org/10.5753/sbcas.2026.21596

Abstract

Biomedical ontologies often fail to capture the lexical variability present in real clinical narratives, limiting Natural Language Processing (NLP) applications over electronic health records. This work proposes a weakly supervised pipeline for adaptive clinical vocabulary expansion using ICD codes from MIMIC-IV v3.1. From 842 normalized root codes, the BioMistral-7B model generated 18,017 candidate terms. After semantic validation using SapBERT embeddings (θ = 0.60), 4,094 terms (22.7%) were retained, representing the highest lexical acceptance rate among the evaluated configurations. In a downstream task of disease mention detection in clinical text (627 diseases), the configuration θ = 0.50 achieved the best empirical performance, increasing macro recall from 4.8% to 21.4% and macro F1 from 2.0% to 5.2%. The results indicate that combining LLM-based lexical generation with embeddingbased semantic validation enables scalable expansion of clinical vocabularies, improving diagnostic coverage in clinical text mining tasks.

Keywords: Natural Language Processing, Language Models, Clinical Vocabulary, Weak Supervision, MIMIC-IV

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