The pseudotemporal bootstrap for predicting glaucoma from cross-sectional visual field data

Progressive loss of the field of vision is characteristic of a number of eye diseases such as glaucoma, a leading cause of irreversible blindness in the world. Recently, there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration, including visual field (VF) test, retinal image, and frequent intraocular pressure measurements. Like the progression of many biological and medical processes, VF progression is inherently temporal in nature. However, many datasets associated with the study of such processes are often cross sectional and the time dimension is not measured due to the expensive nature of such studies. In this paper, we address this issue by developing a method to build artificial time series, which we call pseudo time series from cross-sectional data. This involves building trajectories through all of the data that can then, in turn, be used to build temporal models for forecasting (which would otherwise be impossible without ...

• Type: Journal
• Year: 2010
• Where: TITB
• Authors: Allan Tucker, David Garway-Heath
• Keywords: Disease, Education, Time Series, TITB 2010, Trajectories