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Intelligent Techniques for Analysis of Big Data About Healthcare and Medical Records

Intelligent Techniques for Analysis of Big Data About Healthcare and Medical Records

Pinar Kirci
Copyright: © 2018 |Pages: 24
ISBN13: 9781522532323|ISBN10: 1522532323|EISBN13: 9781522532330
DOI: 10.4018/978-1-5225-3232-3.ch029
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MLA

Kirci, Pinar. "Intelligent Techniques for Analysis of Big Data About Healthcare and Medical Records." Handbook of Research on Promoting Business Process Improvement Through Inventory Control Techniques, edited by Nita H. Shah and Mandeep Mittal, IGI Global, 2018, pp. 559-582. https://doi.org/10.4018/978-1-5225-3232-3.ch029

APA

Kirci, P. (2018). Intelligent Techniques for Analysis of Big Data About Healthcare and Medical Records. In N. Shah & M. Mittal (Eds.), Handbook of Research on Promoting Business Process Improvement Through Inventory Control Techniques (pp. 559-582). IGI Global. https://doi.org/10.4018/978-1-5225-3232-3.ch029

Chicago

Kirci, Pinar. "Intelligent Techniques for Analysis of Big Data About Healthcare and Medical Records." In Handbook of Research on Promoting Business Process Improvement Through Inventory Control Techniques, edited by Nita H. Shah and Mandeep Mittal, 559-582. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-3232-3.ch029

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Abstract

To define huge datasets, the term of big data is used. The considered “4 V” datasets imply volume, variety, velocity and value for many areas especially in medical images, electronic medical records (EMR) and biometrics data. To process and manage such datasets at storage, analysis and visualization states are challenging processes. Recent improvements in communication and transmission technologies provide efficient solutions. Big data solutions should be multithreaded and data access approaches should be tailored to big amounts of semi-structured/unstructured data. Software programming frameworks with a distributed file system (DFS) that owns more units compared with the disk blocks in an operating system to multithread computing task are utilized to cope with these difficulties. Huge datasets in data storage and analysis of healthcare industry need new solutions because old fashioned and traditional analytic tools become useless.

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