Application of a Natural Language Processing Algorithm to Asthma Ascertainment. An Automated Chart Review.

Journal: American journal of respiratory and critical care medicine
Published Date: Aug 15, 2017

Abstract

RATIONALE: Difficulty of asthma ascertainment and its associated methodologic heterogeneity have created significant barriers to asthma care and research.

Authors

  • Chung-Il Wi
    Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minn; Asthma Epidemiology Research Unit, Mayo Clinic, Rochester, Minn.
  • Sunghwan Sohn
    Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, USA.
  • Mary C Rolfes
    2 Asthma Epidemiology Research Unit.
  • Alicia Seabright
    2 Asthma Epidemiology Research Unit.
  • Euijung Ryu
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA.
  • Gretchen Voge
    1 Department of Pediatric and Adolescent Medicine.
  • Kay A Bachman
    6 Division of Allergic Diseases, Mayo Clinic, Mayo Clinic, Rochester, Minnesota.
  • Miguel A Park
    6 Division of Allergic Diseases, Mayo Clinic, Mayo Clinic, Rochester, Minnesota.
  • Hirohito Kita
    6 Division of Allergic Diseases, Mayo Clinic, Mayo Clinic, Rochester, Minnesota.
  • Ivana T Croghan
    7 Department of Medicine Research, Mayo Clinic, Rochester, Minnesota.
  • Hongfang Liu
    Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States.
  • Young J Juhn
    Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minn; Asthma Epidemiology Research Unit, Mayo Clinic, Rochester, Minn. Electronic address: Juhn.young@mayo.edu.

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