Use of machine learning to predict the risk of early morning intraocular pressure peaks in glaucoma patients and suspects.

Camilo Brandão-de-ResendeSebastião CronembergerArtur W VelosoRafael V MerolaCarolina S FreitasÉrica A BorgesAlberto Diniz-Filho

Published in: Arquivos brasileiros de oftalmologia (2021)

The machine learning approach was able to predict the risk of intraocular pressure peaks at 6 a.m. with good accuracy. This new approach to the diurnal intraocular pressure curve may become a widely used tool in daily practice and the indication of a 24-hour intraocular pressure curve could be rationalized according to risk stratification.

Keyphrases

machine learning
end stage renal disease
ejection fraction
chronic kidney disease
primary care
healthcare
blood pressure
prognostic factors
big data
physical activity
deep learning
patient reported outcomes
quality improvement
patient reported
optic nerve