Validation of a Visual Field Prediction Tool for Glaucoma: A Multicenter Study Involving Patients with Glaucoma In the United Kingdom.

Purpose: A previously developed machine-learning approach with Kalman-filtering technology accurately predicted disease trajectory for patients with various glaucoma types and severities using clinical trials data. This study assesses performance of the KF approach with real-world data.

Design: Retrospective cohort study.

Quantile regression forests for individualized surgery scheduling.

Determining the optimal surgical case start times is a challenging stochastic optimization problem that shares a key feature with many other healthcare operations problems. Namely, successful problem solutions require using a vast array of available historical data to create distributions that accurately capture a case duration's uncertainty for integration into an optimization model. Distribution fitting is the conventional approach to generate these distributions, but it can only employ a limited, aggregate portion of the detailed patient features available in Electronic Medical Records systems today.