An innovative methodology for Big Data Visualization for telemedicine
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 hard 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 coloured circles. From a technical point of view, the proposed solution adopts the geoJSON standard to classify data into different circles.
Existing System:
To address issues related to the huge amount of collected data in telemonitoring scenarios, we need to move towards the problem of storing, analysing and visualizing Big Data. It improves access to remote information and services, provides care in rural environment, guarantees quality control of patients’ treatments, and reduces health-care costs. . On the contrary, it reduces communications both a professionals and between professionals and patients. This raises doubts on the quality of health services and presents organizational and bureaucratic difficulties.
Proposed System:
we propose an innovative method for visualizing the patients health status according with measurements coming from different data sources, such as wearable sensors, bloody analysis IoT devices and medical instruments. This method is based on data visualization techniques able to treat distant patients monitoring problem. After having selected a treatment for a specific patient, the doctor or the medical operator is able to follow the evolution of his/her care. Moreover, selecting a zone of interest simply drawing a polygon on the map, it is possible to select one of the included markers for visualizing a patient health status by means of a circular view.
CONCLUSION:
we focused about the Data Visualization aspect a the Big Data steps. More specifically, we investigated a data visualization technique for monitor health status of patients. First of all, we presented the architecture designed and deployed for running the visualization services. From the features point of view, the physicians can monitor treatment of patients drawing a polygon on a showed map, visualizing the markers included in that specific area. Thus, each marker can run a circular view that shows different parameters in order to monitor the status of the health of patients. The experiments performed for different scenarios highlighted great performance of both visualization mode in terms of time as a well as user experience. In future works, for each measurement, we plan to implement a non-linear regression algorithm able to to quantify the missing value with high precision. Furthermore, the development of a Machine Learning techniques would help us to create a model capable of predicting the relative measurement values in order to send alarms to physicians when risky conditions occur.
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