Exploring Visual Attention and Machine Learning in 3D Visualization of Medical Temporal Data

Abstract

Temporal data visualization supports planning and decision-making processes as it helps understanding patterns and relationships among time-based data. In the Healthcare area, the anamnesis procedure offers to physicians a large volume of valuable information, which is usually analyzed considering temporal aspects. Contributing to overcome the limited use of three-dimensional (3D) space, in this article we present a VR approach named 3D Block ARL to support interactive visualization of medical temporal data where the interface design is based on VA concepts. Additionally, we use a rule-based learning method to associate users' preferences to graphical elements aiming to personalize the proposed 3D visualization interface. Our results indicate that VA can be a valuable resource to improve the design of Information Visualization interface tools in the context of temporal medical data as well as to personalize the visualizations according to the preferences of users.