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

Journal: Genetics in medicine : official journal of the American College of Medical Genetics
Published Date: Nov 1, 2022

Abstract

PURPOSE: The 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.

Authors

  • Zeyu Yang
    Department of Orthopedics, Ruijin Hospital LuWan Branch, School of Medicine, Shanghai Jiaotong University School of Medicine, Shanghai, China.
  • Amy Shikany
    Heart Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH.
  • Yizhao Ni
    Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH.
  • Ge Zhang
    Lab of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, People's Republic of China.
  • K Nicole Weaver
    Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH; Heart Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH.
  • Jing Chen
    Department of Vascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China.

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