1.
Stud Health Technol Inform
; 264: 123-127, 2019 Aug 21.
Artigo
em Inglês
| MEDLINE
| ID: mdl-31437898
RESUMO
In this paper, we trained a set of Portuguese clinical word embedding models of different granularities from multi-specialty and multi-institutional clinical narrative datasets. Then, we assessed their impact on a downstream biomedical NLP task of Urinary Tract Infection disease identification. Additionally, we intrinsically evaluated our main model using an adapted version of Bio-SimLex for the Portuguese language. Our empirical results showed that the larger, coarse-grained model achieved a slightly better outcome when compared with the small, fine-grained model in the proposed task. Moreover, we obtained satisfactory results with Bio-SimLex intrinsic evaluation.