Machine Learning-Based Predictive Modeling of Surgical Intervention in Glaucoma Using Systemic Data From Electronic Health Records.

ABSTRACT:

Purpose

To predict the need for surgical intervention in patients with primary open-angle glaucoma (POAG) using systemic data in electronic health records (EHRs).

Design

Development and evaluation of machine learning models.

Methods

Structured EHR data of 385 POAG patients from a single academic institution were incorporated into models using multivariable logistic regression, random forests, and artificial neural networks. Leave-one-out cross-validation was performed. Mean area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and Youden index were calculated for each model to evaluate performance. Systemic variables driving predictions were identified and interpreted.

Results

Multivariable logistic regression was mos

SUBMITTER: Baxter SL 

PROVIDER: S-EPMC6888922 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature