Automatically pre-screening patients for the rare disease aromatic l-amino acid decarboxylase deficiency using knowledge engineering, natural language processing, and machine learning on a large EHR population.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 38134953

Abstract

OBJECTIVES: Electronic health record (EHR) data may facilitate the identification of rare diseases in patients, such as aromatic l-amino acid decarboxylase deficiency (AADCd), an autosomal recessive disease caused by pathogenic variants in the dopa decarboxylase gene. Deficiency of the AADC enzyme results in combined severe reductions in monoamine neurotransmitters: dopamine, serotonin, epinephrine, and norepinephrine. This leads to widespread neurological complications affecting motor, behavioral, and autonomic function. The goal of this study was to use EHR data to identify previously undiagnosed patients who may have AADCd without available training cases for the disease.

Authors

  • Aaron M Cohen
    Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA 97239.
  • Jolie Kaner
    Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health & Science University, Portland, OR 97239, United States.
  • Ryan Miller
    Ronald O Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, 1305 York Ave 6th floor, New York, NY 10021, USA.
  • Jeffrey W Kopesky
    PTC Therapeutics, South Plainfield, NJ 07080, United States.
  • William Hersh
    Department of Medical Informatics & Clinical Epidemiology, School of Medicine, Oregon Health & Science University, Portland, Oregon, United States of America.

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