Automated chart review utilizing natural language processing algorithm for asthma predictive index.

Journal: BMC pulmonary medicine
Published Date: Feb 13, 2018

Abstract

BACKGROUND: Thus far, no algorithms have been developed to automatically extract patients who meet Asthma Predictive Index (API) criteria from the Electronic health records (EHR) yet. Our objective is to develop and validate a natural language processing (NLP) algorithm to identify patients that meet API criteria.

Authors

  • Harsheen Kaur
    Department of Pediatric and Adolescent Medicine, Mayo Clinic, 200 1st Street SW, Rochester, MN, 55905, USA.
  • Sunghwan Sohn
    Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, USA.
  • Chung-Il Wi
    Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minn; Asthma Epidemiology Research Unit, Mayo Clinic, Rochester, Minn.
  • Euijung Ryu
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA.
  • Miguel A Park
    6 Division of Allergic Diseases, Mayo Clinic, Mayo Clinic, Rochester, Minnesota.
  • Kay Bachman
    Division of Allergic Disease, Mayo Clinic, Rochester, MN, USA.
  • Hirohito Kita
    6 Division of Allergic Diseases, Mayo Clinic, Mayo Clinic, Rochester, Minnesota.
  • Ivana Croghan
    Department of Medicine Research, Mayo Clinic, Rochester, MN, USA.
  • Jose A Castro-Rodriguez
    Division of Pediatrics, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile.
  • Gretchen A Voge
    Department of Pediatric and Adolescent Medicine, Mayo Clinic, 200 1st Street SW, Rochester, MN, 55905, USA.
  • 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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