Identifying Factors Associated With Fast Visual Field Progression in Patients With Ocular Hypertension Based on Unsupervised Machine Learning.

Journal: Journal of glaucoma

PMID: 39092996

Abstract

PRCIS: We developed unsupervised machine learning models to identify different subtypes of patients with ocular hypertension in terms of visual field (VF) progression and discovered 4 subtypes with different trends of VF worsening. We then identified factors associated with fast VF progression.

Authors

Xiaoqin Huang

The Pennsylvania State University Great Valley, Malvern, PA, USA.
Asma Poursoroush

Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN.
Jian Sun

Department Of Computer Science, University of Denver, 2155 E Wesley Ave, Denver, Colorado, 80210, United States of America.
Michael V Boland

Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Chris A Johnson

Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA, USA.
Siamak Yousefi

Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America.

Keywords

Aged Cross-Sectional Studies Disease Progression Female Follow-Up Studies Glaucoma, Open-Angle Humans Intraocular Pressure Male Middle Aged Ocular Hypertension Risk Factors Unsupervised Machine Learning Vision Disorders Visual Field Tests Visual Fields

External Resources

View on PubMed Access via DOI PubMed (39092996)

Identifying Factors Associated With Fast Visual Field Progression in Patients With Ocular Hypertension Based on Unsupervised Machine Learning.

Abstract

Authors

Keywords

External Resources

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