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. Author manuscript; available in PMC: 2020 Dec 1.
Published in final edited form as: Am J Ophthalmol. 2019 Jul 16;208:30–40. doi: 10.1016/j.ajo.2019.07.005

Figure 1. Overall workflow diagram.

Figure 1.

This diagram depicts the workflow for machine learning-based predictive modeling to classify patients with primary open-angle glaucoma who need glaucoma surgery within 6 months. After defining the patient cohort, systemic data were extracted from the UCSD electronic health record (EHR) clinical data warehouse, cleaned and processed, and then used for training and testing three different machine learning models: logistic regression, random forests, and artificial neural networks. We employed leave-one-out cross-validation and compared predictive performance between models.