作者
Ruth Johnson, Alexis V Stephens, Rachel Mester, Sergey Knyazev, Lisa A Kohn, Malika K Freund, Leroy Bondhus, Brian L Hill, Tommer Schwarz, Noah Zaitlen, Valerie A Arboleda, Lisa A. Bastarache, Bogdan Pasaniuc, Manish J Butte
发表日期
2024/5/1
期刊
Science Translational Medicine
卷号
16
期号
745
页码范围
eade4510
出版商
American Association for the Advancement of Science
简介
Human inborn errors of immunity include rare disorders entailing functional and quantitative antibody deficiencies due to impaired B cells called the common variable immunodeficiency (CVID) phenotype. Patients with CVID face delayed diagnoses and treatments for 5 to 15 years after symptom onset because the disorders are rare (prevalence of ~1/25,000), and there is extensive heterogeneity in CVID phenotypes, ranging from infections to autoimmunity to inflammatory conditions, overlapping with other more common disorders. The prolonged diagnostic odyssey drives excessive system-wide costs before diagnosis. Because there is no single causal mechanism, there are no genetic tests to definitively diagnose CVID. Here, we present PheNet, a machine learning algorithm that identifies patients with CVID from their electronic health records (EHRs). PheNet learns phenotypic patterns from verified CVID cases …
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