Leveraging weak supervision to perform named entity recognition in electronic health records progress notes to identify the ophthalmology exam.

ABSTRACT:

Objective

To develop deep learning models to recognize ophthalmic examination components from clinical notes in electronic health records (EHR) using a weak supervision approach.

Methods

A corpus of 39,099 ophthalmology notes weakly labeled for 24 examination entities was assembled from the EHR of one academic center. Four pre-trained transformer-based language models (DistilBert, BioBert, BlueBert, and ClinicalBert) were fine-tuned to this named entity recognition task and compared to a baseline regular expression model. Models were evaluated on the weakly labeled test dataset, a human-labeled sample of that set, and a human-labeled independent dataset.

Results

On the weakly labeled test set, all transformer-based models had recall > 0.93, with precision varying from

SUBMITTER: Wang SY 

PROVIDER: S-EPMC9901505 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature