Using natural language processing for identification of herpes zoster ophthalmicus cases to support population-based study.

Journal: Clinical & experimental ophthalmology
PMID: 29920898

Abstract

IMPORTANCE: Diagnosis codes are inadequate for accurately identifying herpes zoster (HZ) ophthalmicus (HZO). There is significant lack of population-based studies on HZO due to the high expense of manual review of medical records.

Authors

  • Chengyi Zheng
    Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California. Electronic address: Chengyi.X.Zheng@kp.org.
  • Yi Luo
    Electrical and Computer Engineering Department, Bioengineering Department, University of California, Los Angeles, CA 90095 USA, and also with the California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA.
  • Cheryl Mercado
    Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California, USA.
  • Lina Sy
    Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California, USA.
  • Steven J Jacobsen
    Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California, USA.
  • Brad Ackerson
    South Bay Medical Center, Kaiser Permanente Southern California, Harbor City, California, USA.
  • Bruno Lewin
    Los Angeles Medical Center, Kaiser Permanente Southern California, Los Angeles, California, USA.
  • Hung Fu Tseng
    Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California, USA.

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