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

Author:

Kirci Pinar1

Affiliation:

1. Istanbul University, Turkey

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.

Publisher

IGI Global

Reference33 articles.

1. Electronic Medical Records and Physician Productivity: Evidence from Panel Data Analysis

2. Branco, M. de O. (2009). Distributed data management for large scale applications (Doctoral Thesis). University of Southampton. Retrieved on May 11, 2016 from http://eprints.soton.ac.uk/72283/

3. Burghard, C. (2012). Big data and analytics key to accountable care success. IDC Health Insights, 1-9. Retrieved on May 10, 2016 from http://www-01.ibm.com/common/ssi/cgi-bin/ssialias?htmlfid=IML14338USEN&appname=skmwww

4. Byrne, E. (2013). Scientists save healthcare (But they are not from med school). Forbes/tech. Retrieved on May 23, 2016 from http://www.forbes.com/sites/netapp/2013/04/17/healthcare-big-data/#18dd12b3553f

5. Chaouchi, H., & Laurent-Maknavicius, M. (2009). Wireless and Mobile Networks Security. Hoboken, NJ: John Wiley & Sons. Retrieved on May 16, 2016 from http://eu.wiley.com/WileyCDA/WileyTitle/productCd-1848211171.html

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