Using Kalman Filtering to Forecast Disease Trajectory for Patients With Normal Tension Glaucoma.

ABSTRACT:

Purpose

To determine whether a machine learning technique called Kalman filtering (KF) can accurately forecast future values of mean deviation (MD), pattern standard deviation, and intraocular pressure for patients with normal tension glaucoma (NTG).

Design

Development and testing of a forecasting model for glaucoma progression.

Methods

We parameterized and validated a KF (KF-NTG) to forecast MD, pattern standard deviation, and intraocular pressure at 24 months into the future using 263 eyes of 263 Japanese patients with NTG. We determined the proportion of patients with MD forecasts within 0.5, 1.0, and 2.5 dBs of the actual values and calculated the root mean squared error (RMSE) for each forecast. We compared KF-NTG with a previously published KF model calibrated u

SUBMITTER: Garcia GP 

PROVIDER: S-EPMC6662653 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature