eprintid: 10782
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/07/82
datestamp: 2023-11-09 16:37:23
lastmod: 2023-11-09 16:37:23
status_changed: 2023-11-09 16:32:11
type: article
metadata_visibility: show
creators_name: Kareem, S.
creators_name: Binti Ahmad, R.
creators_name: Binti Sarlan, A.
creators_name: Hussain, A.
title: Extraction of associations from electronic health records using association rules mining
ispublished: pub
note: cited By 0
abstract: The widespread use of science and technology has generated massive clinical data and made it possible to store these huge volumes of data in the form of electronic health records (EHRs). Big data analytics can be applied on the available enormous amounts of EHRs to extract useful and constructive knowledge. Big data analytics has emerged as a significant area of study for almost all walks of life including one of the most important fields healthcare. Traditional data management system does not have the potential to analyse certain forms of healthcare records but big data analytics serve as a rich source of valuable insights for obtaining the novel associations between the attributes or parameters of EHRs. Although numerous researches have been carried out in the field of healthcare. The aim of this paper is to apply data mining techniques particularly association rules mining on available EHRs in order to extract the possible associations or correlations among the attributes or parameters of laboratory results of complete blood count (CBC) and lipid profile. Based on the research of analysed sample datasets, this paper finds out associations along with multiple associations between parameters. This paper also finds that association rules mining techniques can help policy makers, practitioners and general public to predict trends in healthcare of the given population. © 2018, Institute of Advanced Scientific Research, Inc.. All rights reserved.
date: 2018
publisher: Institute of Advanced Scientific Research, Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053460286&partnerID=40&md5=12f05701c613c61d569953ad5736a465
full_text_status: none
publication: Journal of Advanced Research in Dynamical and Control Systems
volume: 10
number: 10 Spe
pagerange: 1274-1284
refereed: TRUE
issn: 1943023X
citation:   Kareem, S. and Binti Ahmad, R. and Binti Sarlan, A. and Hussain, A.  (2018) Extraction of associations from electronic health records using association rules mining.  Journal of Advanced Research in Dynamical and Control Systems, 10 (10 Spe).  pp. 1274-1284.  ISSN 1943023X