Abstract

The extraction and classification of important information from Spanish Electronic Clinical Narratives (ECNs) can be challenging due to the complexity of the clinical text and the limited availability of labeled data. In this paper, we introduce a chunked Named Entity Recognition model designed to parse and classify sections of ECNs into predefined categories. The model aims to improve section identification and classification accuracy within ECNs in the context of the IberLEF ClinAIS Task. Our system achieves a promising performance, obtaining a weighted B2 score of.6958, demonstrating its capability to accurately distinguish borders and boundaries between sections. The paper concludes with a comprehensive analysis of the results, discussing potential implications and suggesting directions for further improvements in clinical text analysis.