Improving domain adaptation in de-identification of electronic health records through self-training.

ABSTRACT:

Objective

De-identification is a fundamental task in electronic health records to remove protected health information entities. Deep learning models have proven to be promising tools to automate de-identification processes. However, when the target domain (where the model is applied) is different from the source domain (where the model is trained), the model often suffers a significant performance drop, commonly referred to as domain adaptation issue. In de-identification, domain adaptation issues can make the model vulnerable for deployment. In this work, we aim to close the domain gap by leveraging unlabeled data from the target domain.

Materials and methods

We introduce a self-training framework to address the domain adaptation issue by leveraging unlabeled data from the

SUBMITTER: Liao S 

PROVIDER: S-EPMC8449604 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature