Predicting Glaucomatous Progression with Piecewise Regression Model from Heterogeneous Medical Data

This study aims to accurately predict glaucomatous visual-field loss from patient disease data. In general, medical data show two kinds of heterogeneity: 1) internal heterogeneity, in which the phase of disease progression changes in an individual patient’s time series dataset; and 2) external heterogeneity, in which the trends of disease progression differ among patients. Although some previous methods have addressed the external heterogeneity, the internal heterogeneity has never been taken into account in predictions of glaucomatous progression. Here, we developed a novel framework for dealing with the two kinds of heterogeneity to predict glaucomatous progression using a piecewise linear regression (PLR) model. We empirically demonstrate that our method significantly improves the accuracy of predicting visual-field loss compared with existing methods, and can successfully treat the two kinds of heterogeneity often observed in medical data.