Ristevski, Blagoj and Savoska, Snezana (2021) Healthcare and medical Big Data analytics. In: Applications of Big Data in Healthcare Theory and Practice. Academic Press, United Kingdom, pp. 85-112. ISBN 978-0-12-820203-6
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Abstract
In the era of big data, a huge volume of heterogeneous healthcare and medical data are generated daily. These heterogeneous data, that are stored in diverse data formats, have to be integrated and stored in a standard way and format to perform suitable efficient and effective data analysis and visualization. These data, which are generated from different sources such as mobile devices, sensors, lab tests, clinical notes, social media, demographics data, diverse omics data, etc., can be structured, semistructured, or unstructured. These varieties of data structures require these big data to be stored not only in the standard relational databases but also in NoSQL databases. To provide effective data analysis, suitable classification and standardization of big data in medicine and healthcare are necessary, as well as excellent design and implementation of healthcare information systems. Regarding the security and privacy of the patient’s data, we suggest employing suitable data governance policies. Additionally, we suggest choosing of proper software development frameworks, tools, databases, in-database analytics, stream computing and data mining algorithms (supervised, unsupervised and semisupervised) to reveal valuable knowledge and insights from these healthcare and medical big data. Ultimately we propose the development of not only patient-oriented but also decision- and population-centric healthcare information systems.
Item Type: | Book Section |
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Subjects: | Scientific Fields (Frascati) > Natural sciences > Computer and information sciences Scientific Fields (Frascati) > Engineering and Technology > Electrical engineering, electronic engineering,information engineering |
Divisions: | Faculty of Information and Communication Technologies |
Depositing User: | Prof. d-r. Blagoj Ristevski |
Date Deposited: | 25 Dec 2021 16:34 |
Last Modified: | 25 Dec 2021 16:34 |
URI: | https://eprints.uklo.edu.mk/id/eprint/6696 |
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