Identification of an ANCA-Associated Vasculitis Cohort Using Deep Learning and Electronic Health Records.

ABSTRACT:

Background

ANCA-associated vasculitis (AAV) is a rare but serious disease. Traditional case-identification methods using claims data can be time-intensive and may miss important subgroups. We hypothesized that a deep learning model analyzing electronic health records (EHR) can more accurately identify AAV cases.

Methods

We examined the Mass General Brigham (MGB) repository of clinical documentation from 12/1/1979 to 5/11/2021, using expert-curated keywords and ICD codes to identify a large cohort of potential AAV cases. Three labeled datasets (I, II, III) were created, each containing note sections. We trained and evaluated a range of machine learning and deep learning algorithms for note-level classification, using metrics like positive predictive value (PPV), sensitivity,

SUBMITTER: Wang L 

PROVIDER: S-EPMC11213085 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature