A machine learning model identifies patients in need of autoimmune disease testing using electronic health records.

ABSTRACT: Systemic autoimmune rheumatic diseases (SARDs) can lead to irreversible damage if left untreated, yet these patients often endure long diagnostic journeys before being diagnosed and treated. Machine learning may help overcome the challenges of diagnosing SARDs and inform clinical decision-making. Here, we developed and tested a machine learning model to identify patients who should receive rheumatological evaluation for SARDs using longitudinal electronic health records of 161,584 individuals from two institutions. The model demonstrated high performance for predicting cases of autoantibody-tested individuals in a validation set, an external test set, and an independent cohort with a broader case definition. This approach identified more individuals for autoantibody testing compared with c

SUBMITTER: Forrest IS 

PROVIDER: S-EPMC10130143 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature