Clinical Event Detection with Hybrid Neural Architecture

Adyasha Maharana, Meliha Yetisgen


Abstract
Event detection from clinical notes has been traditionally solved with rule based and statistical natural language processing (NLP) approaches that require extensive domain knowledge and feature engineering. In this paper, we have explored the feasibility of approaching this task with recurrent neural networks, clinical word embeddings and introduced a hybrid architecture to improve detection for entities with smaller representation in the dataset. A comparative analysis is also done which reveals the complementary behavior of neural networks and conditional random fields in clinical entity detection.
Anthology ID:
W17-2345
Volume:
BioNLP 2017
Month:
August
Year:
2017
Address:
Vancouver, Canada,
Venues:
BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
351–355
Language:
URL:
https://www.aclweb.org/anthology/W17-2345
DOI:
10.18653/v1/W17-2345
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2345.pdf