Augmenting Kalman Filter Machine Learning Models with Data from OCT to Predict Future Visual Field Loss-Reference-Cited by-同舟云学术

Augmenting Kalman Filter Machine Learning Models with Data from OCT to Predict Future Visual Field Loss

Published:2022-03 Issue:1 Volume:2 Page:100097
ISSN:2666-9145
Container-title:Ophthalmology Science
language:en
Short-container-title:Ophthalmology Science

Author:

Zhalechian Mohammad^ORCID,Van Oyen Mark P.,Lavieri Mariel S.,De Moraes Carlos Gustavo,Girkin Christopher A.,Fazio Massimo A.,Weinreb Robert N.,Bowd Christopher^ORCID,Liebmann Jeffrey M.,Zangwill Linda M.,Andrews Christopher A.,Stein Joshua D.

Publisher

Elsevier BV

Subject

General Materials Science

Reference31 articles.

1. Kalman filtering for spacecraft attitude estimation;Leffens;J Guid Control Dyn,1982

2. Personalized state-space modeling of glucose dynamics for type 1 diabetes using continuously monitored glucose, insulin dose, and meal intake: an extended Kalman filter approach;Wang;J Diabetes Sci Technol,2014

3. The unscented Kalman filter estimates the plasma insulin from glucose measurement;Eberle;Biosystems,2011

4. Using filtered forecasting techniques to determine personalized monitoring schedules for patients with open-angle glaucoma;Schell;Ophthalmology,2014

5. Accuracy of Kalman filtering in forecasting visual field and intraocular pressure trajectory in patients with ocular hypertension;Garcia;JAMA Ophthalmol,2019

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