Automated ICD coding via unsupervised knowledge integration (UNITE).

Journal: International journal of medical informatics
Published Date: Apr 4, 2020

Abstract

OBJECTIVE: Accurate coding is critical for medical billing and electronic medical record (EMR)-based research. Recent research has been focused on developing supervised methods to automatically assign International Classification of Diseases (ICD) codes from clinical notes. However, supervised approaches rely on ICD code data stored in the hospital EMR system and is subject to bias rising from the practice and coding behavior. Consequently, portability of trained supervised algorithms to external EMR systems may suffer.

Authors

  • Aaron Sonabend W
    Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
  • Winston Cai
    Bronx Science, New York City, NY, USA.
  • Yuri Ahuja
    Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
  • Ashwin Ananthakrishnan
    Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, USA.
  • Zongqi Xia
    Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • Sheng Yu
    Medical College, Guangxi University of Science and Technology, Liuzhou, Guangxi, 545005, China.
  • Chuan Hong
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.

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