Natural Language Processing to Quantify Microbial Keratitis Measurements.

Journal: Ophthalmology
Published Date: Dec 1, 2019

Abstract

A natural language processing (NLP) algorithm to extract microbial keratitis morphology measurements from the electronic health record (EHR) was 75-96% sensitive and 91%-96% specific. NLP accurately extracts data from the corneal exam free-text EHR field.

Authors

  • Nenita Maganti
    Department of Ophthalmology and Visual Sciences, W. K. Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan; Feinberg School of Medicine, Northwestern University, Chicago, Illinois.
  • Huan Tan
    Department of Ophthalmology and Visual Sciences, W. K. Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan; Department of Applied Statistics, Rackham Graduate School, University of Michigan, Ann Arbor, Michigan.
  • Leslie M Niziol
    Department of Ophthalmology and Visual Sciences, W. K. Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan.
  • Sejal Amin
    Department of Ophthalmology, Henry Ford Health System, Detroit, Michigan.
  • Andrew Hou
    Department of Ophthalmology, Henry Ford Health System, Detroit, Michigan.
  • Karandeep Singh
    Department of Internal Medicine and School of Information, University of Michigan, Ann Arbor, Michigan.
  • Dena Ballouz
    Department of Ophthalmology and Visual Sciences, W. K. Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan.
  • Maria A Woodward
    Department of Ophthalmology and Visual Sciences, W. K. Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan; Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan. Electronic address: mariawoo@umich.edu.

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