Evaluation of an automated phenotyping algorithm for rheumatoid arthritis.

ABSTRACT: To better understand the challenges of generally implementing and adapting computational phenotyping approaches, the performance of a Phenotype KnowledgeBase (PheKB) algorithm for rheumatoid arthritis (RA) was evaluated on a University of California, Los Angeles (UCLA) patient population, focusing on examining its performance on ambiguous cases. The algorithm was evaluated on a cohort of 4,766 patients, along with a chart review of 300 patients by rheumatologists against accepted diagnostic guidelines. The performance revealed low sensitivity towards specific subtypes of positive RA cases, which suggests revisions in features used for phenotyping. A close examination of select cases also indicated a significant portion of patients with missing data, drawing attention to the need to conside

SUBMITTER: Zheng HW 

PROVIDER: S-EPMC10226612 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature