Using deep learning and electronic health records to detect Noonan syndrome in pediatric patients

Abstract

PurposeThe variable expressivity and multisystem features of Noonan syndrome (NS) make it difficult for patients to obtain a timely diagnosis. Genetic testing can confirm a diagnosis, but underdiagnosis is prevalent owing to a lack of recognition and referral for testing. Our study investigated the utility of using electronic health records (EHRs) to identify patients at high risk of NS. MethodsUsing diagnosis texts extracted from Cincinnati Children’s Hospital’s EHR database, we constructed deep learning models from 162 NS cases and 16,200 putative controls. Performance was evaluated on 2 independent test sets, one containing patients with NS who were previously diagnosed and the other containing patients with undiagnosed NS. ResultsOur novel method performed significantly better than the previous method, with the convolutional neural network model achieving the highest area under the precision-recall curve in both test sets (diagnosed: 0.43, undiagnosed: 0.16). ConclusionThe results suggested the validity of using text-based deep learning methods to analyze EHR and showed the value of this approach as a potential tool to identify patients with features of rare diseases. Given the paucity of medical geneticists, this has the potential to reduce disease underdiagnosis by prioritizing patients who will benefit most from a genetics referral.