Adapting Bidirectional Encoder Representations from Transformers (BERT) to Assess Clinical Semantic Textual Similarity: Algorithm Development and Validation Study.

ABSTRACT:

Background

Natural Language Understanding enables automatic extraction of relevant information from clinical text data, which are acquired every day in hospitals. In 2018, the language model Bidirectional Encoder Representations from Transformers (BERT) was introduced, generating new state-of-the-art results on several downstream tasks. The National NLP Clinical Challenges (n2c2) is an initiative that strives to tackle such downstream tasks on domain-specific clinical data. In this paper, we present the results of our participation in the 2019 n2c2 and related work completed thereafter.

Objective

The objective of this study was to optimally leverage BERT for the task of assessing the semantic textual similarity of clinical text data.

Methods

We used BERT as an initial

SUBMITTER: Kades K 

PROVIDER: S-EPMC7889424 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature