换一批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.
第一作者：
Mohammad,Zhalechian
第一单位：
Department of Industrial and Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan.
作者：
关键词
AD, African descentADAGES, African Descent and Glaucoma Evaluation StudyAlgorithm biasCI, confidence intervalD, diopterDIGS, Diagnostic Innovation in Glaucoma StudyED, European descentGlaucomaIOP, intraocular pressureKF, Kalman filterKF-TP, Kalman filter with tonometry and perimetry dataKF-TPO, Kalman filter with tonometry, perimetry, and global retinal nerve fiber layer dataKalman filterLR1, linear regression model 1LR2, linear regression model 2MAE, mean absolute errorMD, mean deviationMachine learningOAG, open-angle glaucomaOCTPSD, pattern standard deviationRMSE, root mean square errorRNFL, retinal nerve fiber layerSD, standard deviationVF, visual field
DOI
10.1016/j.xops.2021.100097
PMID
36246178
发布时间
2024-02-28
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Ophthalmology science
2022年2卷1期
100097页
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