The Chilean Waiting List Corpus: a new resource for clinical Named Entity Recognition in Spanish

Abstract

In this work we describe the Waiting List Corpus consisting of de-identified referrals for several specialty consultations from the waiting list in Chilean public hospitals. A subset of 900 referrals was manually annotated with 9,029 entities, 385 attributes, and 284 pairs of relations with clinical relevance. A trained medical doctor annotated these referrals, and then together with other three researchers, consolidated each of the annotations. The annotated corpus has nested entities, with 32.2% of entities embedded in other entities. We use this annotated corpus to obtain preliminary results for Named Entity Recognition (NER). The best results were achieved by using a biLSTM-CRF architecture using word embeddings trained over Spanish Wikipedia together with clinical embeddings computed by the group. NER models applied to this corpus can leverage statistics of diseases and pending procedures within this waiting list. This work constitutes the first annotated corpus using clinical narratives from Chile, and one of the few for the Spanish language. The annotated corpus, the clinical word embeddings, and the annotation guidelines are freely released to the research community.

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

“…Our group has experience using static word embeddings for patient classification deployed in a hospital (Villena et al, 2021b ) and using stacked embeddings that combine both static and contextualized embeddings for named entity recognition (Báez et al, 2020 ; Báez et al, 2022 ) and automatic coding (Villena et al, 2021a ). Evaluating the automatic translation of clinical sentences has pointed us the need for creating reliable intrinsic tests created from scratch for the Spanish language, which can be valuable for both static and contextual word embeddings.…”

“…For the computation of word embeddings a training corpus is needed. For this reason, we extracted data from three sources: (1) clinical narratives from The Chilean Waiting List Corpus, which is a collection of diagnostic suspicions from the waiting list in Chilean public hospitals (Báez et al, 2020 ; Báez et al, 2022 ; Villena et al, 2021b ), (2) a medical journal corpus extracted from the SciELO library, which is a collection of articles from several medical journals in Spanish (Villena et al, 2020 ) and (3) a corpus constructed from the Unified Medical Language System (UMLS) (Bodenreider, 2004 ) term graph. We computed embeddings using Word2vec and fastText algorithms and validated them using classic intrinsic evaluation tests adapted to Spanish, such as word pair similarity and semantic textual similarity.…”

Clinical Flair: A Pre-Trained Language Model for Spanish Clinical Natural Language Processing

“…These models trained on a large corpus obtained from the Spanish Wikipedia are freely available in the Flair framework (Akbik et al, 2019). To incorporate key information from the clinical context, we fine-tuned these models on the Chilean Waiting List corpus (Báez et al, 2020), which is a clinical corpus created from real diagnoses from the Chilean public healthcare system.…”

Proceedings of the 4th Clinical Natural Language Processing Workshop

“…These models trained on a large corpus obtained from the Spanish Wikipedia are freely available in the Flair framework (Akbik et al, 2019). To incorporate key information from the clinical context, we fine-tuned these models on the Chilean Waiting List corpus (Báez et al, 2020), which is a clinical corpus created from real diagnoses from the Chilean public healthcare system.…”