Predicting Glaucoma Surgical Outcomes Using Neural Networks and Machine Learning on Electronic Health Records.

Journal: Translational vision science & technology

PMID: 38904612

Abstract

PURPOSE: To develop machine learning (ML) and deep learning (DL) models to predict glaucoma surgical outcomes, including postoperative intraocular pressure, use of ocular antihypertensive medications, and need for repeat surgery.

Authors

Samuel Barry

Department of Management Science & Engineering, Stanford University, Stanford, CA, USA.
Sophia Y Wang

School of Medicine, Stanford University, Palo Alto, CA, United States.

Keywords

Aged Antihypertensive Agents Deep Learning Electronic Health Records Female Glaucoma Humans Intraocular Pressure Machine Learning Male Middle Aged Neural Networks, Computer Retrospective Studies ROC Curve Treatment Outcome

External Resources

View on PubMed Access via DOI PubMed (38904612)

Predicting Glaucoma Surgical Outcomes Using Neural Networks and Machine Learning on Electronic Health Records.

Abstract

Authors

Keywords

External Resources

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