Medikuntza arloko eta arlo juridikoko hizkuntzaren prozesamendua

Biomedical literature is one of the most relevant sources of information for knowledge mining in the field of Bioinformatics. In spite of English being the most widely addressed language in the field; in recent years, there has been a growing interest from the natural language processing community in dealing with languages other than English. However, the availability of language resources and tools for appropriate treatment of non-English texts is lacking behind.

This paper introduces the first version of the NUB ES corpus (Negation and Uncertainty annotations in Biomedical texts in Spanish). The corpus is part of an on-going research and currently consists of 29,682 sentences obtained from anonymised health records annotated with negation and uncertainty. The article includes an exhaustive comparison with similar corpora in Spanish, and presents the main annotation and design decisions. Additionally, we perform preliminary experiments using deep learning algorithms to validate the annotated dataset.

The aim of this paper is to present our approach (MEDIA)on the CLEF-2020 eHealth Task 1. The task consists in automatically assigning ICD10 codes (CIE-10, in Spanish) to clinical case documents,evaluating the prediction against manually generated ICD10 codifications. Our system took part in two different subtasks: one corresponding to Diagnosis Coding (CodiEsp-D) and the other to Procedure Coding(CodiEsp-P).