Identification of asthma control factor in clinical notes using a hybrid deep learning model.

Journal: BMC medical informatics and decision making
Published Date: Nov 9, 2021

Abstract

BACKGROUND: There are significant variabilities in guideline-concordant documentation in asthma care. However, assessing clinician's documentation is not feasible using only structured data but requires labor-intensive chart review of electronic health records (EHRs). A certain guideline element in asthma control factors, such as review inhaler techniques, requires context understanding to correctly capture from EHR free text.

Authors

  • Bhavani Singh Agnikula Kshatriya
    Department of Artificial Intelligence and Informatics, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
  • Elham Sagheb
    Department of Artificial Intelligence and Informatics, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
  • Chung-Il Wi
    Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minn; Asthma Epidemiology Research Unit, Mayo Clinic, Rochester, Minn.
  • Jungwon Yoon
    Department of Pediatrics, Myongji Hospital, Goyang, South Korea.
  • Hee Yun Seol
    Community Pediatrics and Adolescent Medicine, Mayo Clinic, Rochester, Minnesota, USA.
  • Young Juhn
    Department of Pediatrics, Mayo Clinic, Rochester, MN, United States.
  • Sunghwan Sohn
    Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, USA.

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