Journal Article

Hybrid natural language processing tool for semantic annotation of medical texts in Spanish

Capllonch-Carrión, Adrián, Valverde-Mateos, Ana, Campillos-Llanos, Leonardo

2025

Overview

Background Natural language processing (NLP) enables the extraction of information embedded within unstructured texts, such as clinical case reports and trial eligibility criteria. By identifying relevant medical concepts, NLP facilitates the generation of structured and actionable data, supporting complex tasks like cohort identification and the analysis of clinical records. To accomplish those tasks, we introduce a deep learning-based and lexicon-based named entity recognition (NER) tool for texts in Spanish. It performs medical NER and normalization, medication information extraction and detection of temporal entities, negation and speculation, and temporality or experiencer attributes (Age, Contraindicated, Negated, Speculated, Hypothetical, Future, Family_member, Patient and Other). We built the tool with a dedicated lexicon and rules adapted from NegEx and HeidelTime. Using these resources, we annotated a corpus of 1200 texts, with high inter-annotator agreement (average F1 = 0.841% ± 0.045 for entities, and average F1 = 0.881% ± 0.032 for attributes). We used this corpus to train Transformer-based models (RoBERTa-based models, mBERT and mDeBERTa). We integrated them with the dictionary-based system in a hybrid tool, and distribute the models via the Hugging Face hub. For an internal validation, we used a held-out test set and conducted an error analysis. For an external validation, eight medical professionals evaluated the system by revising the annotation of 200 new texts not used in development. Results In the internal validation, the models yielded F1 values up to 0.915. In the external validation with 100 clinical trials, the tool achieved an average F1 score of 0.858 (± 0.032); and in 100 anonymized clinical cases, it achieved an average F1 score of 0.910 (± 0.019). Conclusions The tool is available at https://claramed.csic.es/medspaner . We also release the code ( https://github.com/lcampillos/medspaner ) and the annotated corpus to train the models.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Algorithms / Analysis / Annotations / Automation / Bioinformatics / Biomedical and Life Sciences / Case reports / Clinical trials / Computational Biology/Bioinformatics / Computational linguistics / Computer Appl. in Life Sciences / Data analysis / Data mining / Data Mining - methods / Deep Learning / Deep learning in healthcare / Dictionaries / Disease / Electronic Health Records / Error analysis / Humans / Information processing / Information retrieval / Language / Language processing / Life Sciences / Machine learning / Medical literature / Medical natural language processing / Medical personnel / Medical text mining tool / Methods / Microarrays / Named entity recognition / Natural language interfaces / Natural Language Processing / Product reviews / Python / Semantics / Software / Spain / Spanish language / Spanish medical NLP / Task complexity / Texts / Unstructured data