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

Iain S. Forrest, Ben O. Petrazzini, Áine Duffy, Joshua K. Park, Anya J. O’Neal, Daniel M. Jordan, Ghislain Rocheleau, Girish N. Nadkarni, Judy H. Cho, Ashira D. Blazer and Ron Do ()
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Iain S. Forrest: Icahn School of Medicine at Mount Sinai
Ben O. Petrazzini: Icahn School of Medicine at Mount Sinai
Áine Duffy: Icahn School of Medicine at Mount Sinai
Joshua K. Park: Icahn School of Medicine at Mount Sinai
Anya J. O’Neal: University of Maryland School of Medicine
Daniel M. Jordan: Icahn School of Medicine at Mount Sinai
Ghislain Rocheleau: Icahn School of Medicine at Mount Sinai
Girish N. Nadkarni: Icahn School of Medicine at Mount Sinai
Judy H. Cho: Icahn School of Medicine at Mount Sinai
Ashira D. Blazer: Hospital for Special Surgery
Ron Do: Icahn School of Medicine at Mount Sinai

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: 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 current clinical standards and a greater proportion of autoantibody carriers among those tested. Diagnoses of SARDs and other autoimmune conditions increased with higher model probabilities. The model detected a need for autoantibody testing and rheumatology encounters up to five years before the test date and assessment date, respectively. Altogether, these findings illustrate that the clinical manifestations of a diverse array of autoimmune conditions are detectable in electronic health records using machine learning, which may help systematize and accelerate autoimmune testing.

Date: 2023
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DOI: 10.1038/s41467-023-37996-7

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