Enhancing clinical concept extraction with contextual embeddings.

ABSTRACT:

Objective

Neural network-based representations ("embeddings") have dramatically advanced natural language processing (NLP) tasks, including clinical NLP tasks such as concept extraction. Recently, however, more advanced embedding methods and representations (eg, ELMo, BERT) have further pushed the state of the art in NLP, yet there are no common best practices for how to integrate these representations into clinical tasks. The purpose of this study, then, is to explore the space of possible options in utilizing these new models for clinical concept extraction, including comparing these to traditional word embedding methods (word2vec, GloVe, fastText).

Materials and methods

Both off-the-shelf, open-domain embeddings and pretrained clinical embeddings from MIMIC-III (Medical I

SUBMITTER: Si Y 

PROVIDER: S-EPMC6798561 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature