The OpenDeID corpus for patient de-identification.

ABSTRACT: For research purposes, protected health information is often redacted from unstructured electronic health records to preserve patient privacy and confidentiality. The OpenDeID corpus is designed to assist development of automatic methods to redact sensitive information from unstructured electronic health records. We retrieved 4548 unstructured surgical pathology reports from four urban Australian hospitals. The corpus was developed by two annotators under three different experimental settings. The quality of the annotations was evaluated for each setting. Specifically, we employed serial annotations, parallel annotations, and pre-annotations. Our results suggest that the pre-annotations approach is not reliable in terms of quality when compared to the serial annotations but can drastically

SUBMITTER: Jonnagaddala J 

PROVIDER: S-EPMC8497517 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature