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,
- Editors:
- Kevin Bretonnel Cohen, Dina Demner-Fushman, Sophia Ananiadou, Junichi Tsujii
- Venue:
- BioNLP
- SIG:
- SIGBIOMED
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 351–355
- Language:
- URL:
- https://aclanthology.org/W17-2345
- DOI:
- 10.18653/v1/W17-2345
- Cite (ACL):
- Adyasha Maharana and Meliha Yetisgen. 2017. Clinical Event Detection with Hybrid Neural Architecture. In BioNLP 2017, pages 351–355, Vancouver, Canada,. Association for Computational Linguistics.
- Cite (Informal):
- Clinical Event Detection with Hybrid Neural Architecture (Maharana & Yetisgen, BioNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W17-2345.pdf
- Data
- MIMIC-III