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Title Automatic Extraction of Nested Entities in Clinical Referrals in Spanish
Authors Pablo Báez, Felipe Bravo-Marquez, Jocelyn Dunstan, Matías Rojas, Fabian Villena
Publication date July 2022
Abstract Here we describe a new clinical corpus rich in nested
entities
and a series of neural models to identify them. The corpus comprises
de-identified referrals from the waiting list in Chilean public hospitals. A
subset of 5,000 referrals (58.6% medical and 41.4% dental) was manually
annotated with 10 types of entities, six attributes, and pairs of relations
with clinical relevance. In total, there are 110,771 annotated tokens. A
trained medical doctor or dentist annotated these referrals, and then,
together with three other researchers, consolidated each of the annotations.
The annotated corpus has 48.17% of entities embedded in other entities or
containing another one. We use this corpus to build models for Named Entity
Recognition (NER). The best results were achieved using a Multiple
Single-entity architecture with clinical word embeddings stacked with
character and Flair contextual embeddings. The entity with the best
performance is abbreviation, and the hardest to recognize is finding. NER
models applied to this corpus can leverage statistics of diseases and
pending procedures. This work constitutes the first annotated corpus using
clinical narratives from Chile and one of the few in Spanish. The annotated
corpus, clinical word embeddings, annotation guidelines, and neural models
are freely released to the community.
Pages 1-22
Volume 3
Journal name ACM Transactions on Computing for Healthcare
Publisher ACM Press (New York, NY, USA)
Reference URL View reference page