With the explosion of Big Data, visualizing statistical data became a challenging topic that has involved many research efforts over the last years. Interpreting Big Data and efficiently showing information for good understanding are difficult tasks, especially in healthcare scenarios, where different types of data have to been managed and cross-related. Some models and techniques for health data visualization have been presented in literature. However, they do not satisfy the visualization needs of physicians and medical personnel. In this paper, we present a new graphical tool for the visualization of health data, that can be easily used for monitoring health status of patients remotely. The tool is very user friendly, and allows physician to quickly understand the current status of a person by looking at colored circles. From a technical point of view, the proposed solution adopts the geoJSON standard to classify data into different circles.

An Innovative Methodology for Big Data Visualization for Telemedicine

Bramanti A.;
2019-01-01

Abstract

With the explosion of Big Data, visualizing statistical data became a challenging topic that has involved many research efforts over the last years. Interpreting Big Data and efficiently showing information for good understanding are difficult tasks, especially in healthcare scenarios, where different types of data have to been managed and cross-related. Some models and techniques for health data visualization have been presented in literature. However, they do not satisfy the visualization needs of physicians and medical personnel. In this paper, we present a new graphical tool for the visualization of health data, that can be easily used for monitoring health status of patients remotely. The tool is very user friendly, and allows physician to quickly understand the current status of a person by looking at colored circles. From a technical point of view, the proposed solution adopts the geoJSON standard to classify data into different circles.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4771565
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