Subcategorizing EHR diagnosis codes to improve clinical application of machine learning models.

Journal: International journal of medical informatics
Published Date: Sep 21, 2021

Abstract

BACKGROUND: Electronic health record (EHR) data is commonly used for secondary purposes such as research and clinical decision support. However, reuse of EHR data presents several challenges including but not limited to identifying all diagnoses associated with a patient's clinical encounter. The purpose of this study was to assess the feasibility of developing a schema to identify and subclassify all structured diagnosis codes for a patient encounter.

Authors

  • Andrew P Reimer
    Case Western Reserve University Frances Payne Bolton School of Nursing, Cleveland, OH; Cleveland Clinic Critical Care Transport, Cleveland, OH.
  • Wei Dai
    Department of Intensive Care Unit, The First Affiliated Hospital of Jiangxi Medical College, Shangrao, Jiangxi, China.
  • Benjamin Smith
    Department of Mathematics, Applied Mathematics and Statistics, College of Arts and Sciences, Case Western Reserve University, Cleveland, OH, United States.
  • Nicholas K Schiltz
    *Department of Epidemiology & Biostatistics, Case Western Reserve University School of Medicine, Cleveland, OH †Department of Sociology, University of Nebraska-Lincoln, Lincoln, NE ‡Department of Health Policy and Management, George Washington University Milken Institute School of Public Health, Washington, DC §Department of Family Medicine, Michigan State University, East Lansing, MI ∥Department of Family Medicine and Community Health, Case Western Reserve University School of Medicine, Cleveland, OH.
  • Jiayang Sun
  • Siran M Koroukian

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