eprintid: 12125
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/21/25
datestamp: 2023-11-10 03:26:40
lastmod: 2023-11-10 03:26:40
status_changed: 2023-11-10 01:16:56
type: article
metadata_visibility: show
creators_name: Thangarasu, G.
creators_name: Subramanian, K.
title: Big data analytics for improved care delivery in the healthcare industry
ispublished: pub
note: cited By 4
abstract: The big data analytics plays a pivotal role in the field of healthcare services and research to facilitate better service to the patients. It has provided tools to accumulate, manage, analysis the structured and unstructured data produced by the healthcare systems. Recently the utilization of big data analytics has been increased in the healthcare industry for assisting the process of diagnosing diseases and care delivery. However, the adoption and research development of big data analysis in the healthcare industry is still slow down due to facing some fundamental problems inherent within the big data paradigm. In this study, addresses these problems which focus on the upcoming and promising areas of medical research and proposed a novel big data analytics approach using Apache Spark. The proposed approach will improve care delivery in the healthcare industry. Big data analytics can continually evaluate clinical data in order to improve the effective practices of physicians and improved patient care. © 2019 iJOE.
date: 2019
publisher: Kassel University Press GmbH
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068166070&doi=10.3991%2fijoe.v15i10.10875&partnerID=40&md5=7b1563e14b463f135448160cd59b0ee5
id_number: 10.3991/ijoe.v15i10.10875
full_text_status: none
publication: International journal of online and biomedical engineering
volume: 15
number: 10
pagerange: 40-51
refereed: TRUE
issn: 26268493
citation:   Thangarasu, G. and Subramanian, K.  (2019) Big data analytics for improved care delivery in the healthcare industry.  International journal of online and biomedical engineering, 15 (10).  pp. 40-51.  ISSN 26268493