Publicly Available Clinical BERT Embeddings

Emily Alsentzer, John Murphy, William Boag, Wei-Hung Weng, Di Jindi, Tristan Naumann, Matthew McDermott


Abstract
Contextual word embedding models such as ELMo and BERT have dramatically improved performance for many natural language processing (NLP) tasks in recent months. However, these models have been minimally explored on specialty corpora, such as clinical text; moreover, in the clinical domain, no publicly-available pre-trained BERT models yet exist. In this work, we address this need by exploring and releasing BERT models for clinical text: one for generic clinical text and another for discharge summaries specifically. We demonstrate that using a domain-specific model yields performance improvements on 3/5 clinical NLP tasks, establishing a new state-of-the-art on the MedNLI dataset. We find that these domain-specific models are not as performant on 2 clinical de-identification tasks, and argue that this is a natural consequence of the differences between de-identified source text and synthetically non de-identified task text.
Anthology ID:
W19-1909
Volume:
Proceedings of the 2nd Clinical Natural Language Processing Workshop
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Venues:
ClinicalNLP | NAACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
72–78
Language:
URL:
https://aclanthology.org/W19-1909
DOI:
10.18653/v1/W19-1909
Bibkey:
Cite (ACL):
Emily Alsentzer, John Murphy, William Boag, Wei-Hung Weng, Di Jindi, Tristan Naumann, and Matthew McDermott. 2019. Publicly Available Clinical BERT Embeddings. In Proceedings of the 2nd Clinical Natural Language Processing Workshop, pages 72–78, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
Publicly Available Clinical BERT Embeddings (Alsentzer et al., 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-1909.pdf
Code
 EmilyAlsentzer/clinicalBERT +  additional community code