Using Kalman Filtering to Forecast Disease Trajectory for Patients With Normal Tension Glaucoma.

Journal: American journal of ophthalmology
Published Date: Mar 1, 2019

Abstract

PURPOSE: To determine whether a machine learning technique called Kalman filtering (KF) can accurately forecast future values of mean deviation (MD), pattern standard deviation, and intraocular pressure for patients with normal tension glaucoma (NTG).

Authors

  • Gian-Gabriel P Garcia
    Department of Industrial and Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan, USA.
  • Koji Nitta
    Fukui-ken Saiseikai Hospital, Fukui, Japan; Kanazawa University Graduate School of Medical Science, Kanazawa, Japan.
  • Mariel S Lavieri
    Department of Industrial and Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan.
  • Chris Andrews
    Department of Ophthalmology and Visual Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA; Center for Eye Policy and Innovation, University of Michigan, Ann Arbor, Michigan, USA.
  • Xiang Liu
    College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, China; Anhui Provincial Key Laboratory of Environmental Pollution Control and Resource Reuse, Anhui Jianzhu University, Hefei 230009, China.
  • Elizabeth Lobaza
    Department of Industrial and Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan, USA.
  • Mark P Van Oyen
    Department of Industrial and Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan.
  • Kazuhisa Sugiyama
    Kanazawa University Graduate School of Medical Science, Kanazawa, Japan.
  • Joshua D Stein
    Department of Ophthalmology & Visual Sciences, University of Michigan Kellogg Eye Center, Ann Arbor, Michigan.

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