Authors:
Kyosuke Tomoda
;
Kai Morino
;
Hiroshi Murata
;
Ryo Asaoka
and
Kenji Yamanishi
Affiliation:
The University of Tokyo, Japan
Keyword(s):
Glaucoma, Intraocular Pressure, Heterogeneity, Collective Method, Piecewise Linear Regression Model.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Development of Assistive Technology
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Knowledge-Based Systems
;
Medical and Nursing Informatics
;
Pattern Recognition and Machine Learning
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
Abstract:
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.