Publicly Available Clinical BERT Embeddings

TL;DR

This paper introduces and releases publicly available BERT embeddings trained specifically on clinical text, demonstrating improved performance on several clinical NLP tasks, with some limitations on de-identification tasks.

Contribution

The paper provides the first publicly available clinical BERT models for generic and discharge summaries, enhancing NLP performance in the clinical domain.

Findings

Domain-specific models improve clinical NLP task performance

Models are less effective on de-identification tasks

Releasing these models supports further clinical NLP research

Abstract

Contextual word embedding models such as ELMo (Peters et al., 2018) and BERT (Devlin et al., 2018) 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 three common clinical NLP tasks as compared to nonspecific embeddings. These domain-specific models are not as performant on two 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.