Communication
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This version is not peer-reviewed
Visual Analytics on Biomedical Dark Data
Version 1
: Received: 27 October 2020 / Approved: 28 October 2020 / Online: 28 October 2020 (07:47:26 CET)
How to cite: Aggarwal, S.; Singh, R. Visual Analytics on Biomedical Dark Data. Preprints 2020, 2020100567 Aggarwal, S.; Singh, R. Visual Analytics on Biomedical Dark Data. Preprints 2020, 2020100567
Abstract
Over the years, there has been a significant rise in the world's scientific knowledge. However, most of it lacks structure and is often termed as Dark Data. Both humans and expert systems have continually faced difficulty in analyzing and comprehending such overwhelming amounts of information which is crucial in solving several real-world problems. Information and data visualization techniques proffer a promising solution to explore such data by allowing quick comprehension of information, the discovery of emerging trends, identification of relationships and patterns, etc. In this tutorial, we utilize the rich corpus of PubMed comprising of more than 30 million citations from biomedical literature to visually explore and understand the underlying key-insights using various information visualization techniques. With this study, we aim to diminish the limitation of human cognition and perception in handling and examining such large volumes of data by speeding up the process of decision making and pattern recognition and enabling decision-makers to fully understand data insights and make informed decisions.
Keywords
Data Visualization; Visual Analytics; Natural Language Processing; Dark Data; Pattern Recognition
Subject
Computer Science and Mathematics, Algebra and Number Theory
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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