Training and intrinsic evaluation of lightweight word embeddings for the clinical domain in Spanish

Abstract

Resources for Natural Language Processing (NLP) are less numerous for languages different from English. In the clinical domain, where these resources are vital for obtaining new knowledge about human health and diseases, creating new resources for the Spanish language is imperative. One of the most common approaches in NLP is word embeddings, which are dense vector representations of a word, considering the word's context. This vector representation is usually the first step in various NLP tasks, such as text classification or information extraction. Therefore, in order to enrich Spanish language NLP tools, we built a Spanish clinical corpus from waiting list diagnostic suspicions, a biomedical corpus from medical journals, and term sequences sampled from the Unified Medical Language System (UMLS). These three corpora can be used to compute word embeddings models from scratch using Word2vec and fastText algorithms. Furthermore, to validate the quality of the calculated embeddings, we adapted several evaluation datasets in English, including some tests that have not been used in Spanish to the best of our knowledge. These translations were validated by two bilingual clinicians following an ad hoc validation standard for the translation. Even though contextualized word embeddings nowadays receive enormous attention, their calculation and deployment require specialized hardware and giant training corpora. Our static embeddings can be used in clinical applications with limited computational resources. The validation of the intrinsic test we present here can help groups working on static and contextualized word embeddings. We are releasing the training corpus and the embeddings within this publication1.

Year 2022 in Medical Natural Language Processing: Availability of Language Models as a Step in the Democratization of NLP in the Biomedical Area

“…• Malayalam: generation of synoptic clinical reports [39]; • Polish: prediction of cardiovascular diseases in electronic health records [40]; • (Brazilian) Portuguese: description of an annotated clinical corpus [41], ICD-10 coding [42]; • Serbian: sentiment analysis in COVID-19 tweets [43]; • Spanish: ICD-coding [10, 44], negation and uncertainty detection in clinical narratives [45], training and evaluation of word embeddings for the clinical domain [46]; • Swedish: ICD-10 coding [44];…”