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

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.

CLARA-MeD Tool - A System to Help Patients Understand Clinical Trial Announcements and Consent Forms in Spanish.

We present an NLP web-based tool to help users understand consent forms (CFs) and clinical trial announcements (CTAs) in Spanish. For complex word identification, we collected: 1) a lexicon of technical terms and simplified synonyms (14 465 entries); and 2) a glossary (70 547 terms) with explanations from sources such as UMLS, the NCI dictionary, Orphadata or the FDA. For development, we extracted entities from 60 CTAs, 60 CFs and 60 patient information documents (PIDs).

A clinical trials corpus annotated with UMLS entities to enhance the access to evidence-based medicine.

Background: The large volume of medical literature makes it difficult for healthcare professionals to keep abreast of the latest studies that support Evidence-Based Medicine. Natural language processing enhances the access to relevant information, and gold standard corpora are required to improve systems. To contribute with a new dataset for this domain, we collected the Clinical Trials for Evidence-Based Medicine in Spanish (CT-EBM-SP) corpus.