Contextualized race and ethnicity annotations for clinical text from MIMIC-III.

ABSTRACT: Observational health research often relies on accurate and complete race and ethnicity (RE) patient information, such as characterizing cohorts, assessing quality/performance metrics of hospitals and health systems, and identifying health disparities. While the electronic health record contains structured data such as accessible patient-level RE data, it is often missing, inaccurate, or lacking granular details. Natural language processing models can be trained to identify RE in clinical text which can supplement missing RE data in clinical data repositories. Here we describe the Contextualized Race and Ethnicity Annotations for Clinical Text (C-REACT) Dataset, which comprises 12,000 patients and 17,281 sentences from their clinical notes in the MIMIC-III dataset. Using these sentences, tw

SUBMITTER: Bear Don't Walk OJ 

PROVIDER: S-EPMC11621419 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature