Augmenting Kalman Filter Machine Learning Models with Data from OCT to Predict Future Visual Field Loss: An Analysis Using Data from the African Descent and Glaucoma Evaluation Study and the Diagnostic Innovation in Glaucoma Study

PURPOSE: To assess whether the predictive accuracy of machine learning algorithms using Kalman filtering for forecasting future values of global indices on perimetry can be enhanced by adding global retinal nerve fiber layer (RNFL) data and whether model performance is influenced by the racial compo...

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Detalles Bibliográficos
Autores principales: Zhalechian, Mohammad, Van Oyen, Mark P., Lavieri, Mariel S., De Moraes, Carlos Gustavo, Girkin, Christopher A., Fazio, Massimo A., Weinreb, Robert N., Bowd, Christopher, Liebmann, Jeffrey M., Zangwill, Linda M., Andrews, Christopher A., Stein, Joshua D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9560647/
https://www.ncbi.nlm.nih.gov/pubmed/36246178
http://dx.doi.org/10.1016/j.xops.2021.100097

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9560647/
https://www.ncbi.nlm.nih.gov/pubmed/36246178
http://dx.doi.org/10.1016/j.xops.2021.100097

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