VISUAL ANALYTICS — AN EFFECTIVE TECHNOLOGY FOR PROCESSING BIG DATA IN MEDICINE
DOI:
https://doi.org/10.11603/mie.1996-1960.2020.2.11173Keywords:
visual analytics, Big Data, medicineAbstract
Background. The analysis of the prospects for the use of visual analytics in clinical and experimental medicine, a healthcare, pharmacy and clinical research, primarily for processing Big Data, is presented. The analysis shows that visual analytics provides a more accessible and intuitive approach to the analysis of biomedical information, improves the efficiency of the use of collected and accumulated data, and reveals new and unknown knowledge by finding relationships, patterns, trends and anomalies in Big Data. Visual analytics provide data management, research and analysis. The developed methods for presenting data in the form of images, diagrams are aimed at the most complete use of registers of medical data, the use of accumulated information to predict the possibility of the development of diseases and their prevention, and in general should contribute to solving the problems of information overload. The above data indicate that the technology of visual analytics will contribute to a significant improvement in the quality of medical care for the population. The purpose of the study is to analyze the current state, problems and prospects of using Visual analytics technologies for Big Data analysis in medicine.
Materials and methods. Results. Visual analytics has great potential for Big Data processing, it allows for comprehensive data analysis to improve the quality of medical care and the analysis of a large number of complex heterogeneous data of various nature. Visual analytics allows you to increase the efficiency of using the collected and accumulated data, to discover new and unknown knowledge by finding relationships, patterns, trends and anomalies in Big Data. The use of VA will allow to solve the problem of information overload, which occurs when analyzing huge data arrays, to establish a relationship between a large number of variables.
Conclusions. VA methods allow clinicians, researchers, administrators, and patients to obtain effective, meaningful information from the vast and complex data resources that a modern healthcare system offers. At the same time, there are problems with the visual study of unstructured data, the lack of standardized methods for assessing, validating and measuring the effectiveness of IA tools, which requires further research.
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