Document Detail


Diagnostic analysis of patients with essential hypertension using association rule mining.
MedLine Citation:
PMID:  21818427     Owner:  NLM     Status:  PubMed-not-MEDLINE    
Abstract/OtherAbstract:
OBJECTIVES: The purpose of this study was to analyze the records of patients diagnosed with essential hypertension using association rule mining (ARM).
METHODS: Patients with essential hypertension (ICD code, I10) were extracted from a hospital's data warehouse and a data mart constructed for analysis. Apriori modeling of the ARM method and web node in the Clementine 12.0 program were used to analyze patient data.
RESULTS: Patients diagnosed with essential hypertension totaled 5,022 and the diagnostic data extracted from those patients numbered 53,994. As a result of the web node, essential hypertension, non-insulin dependent diabetes mellitus (NIDDM), and cerebral infarction were shown to be associated. Based on the results of ARM, NIDDM (support, 35.15%; confidence, 100%) and cerebral infarction (support, 21.21%; confidence, 100%) were determined to be important diseases associated with essential hypertension.
CONCLUSIONS: Essential hypertension was strongly associated with NIDDM and cerebral infarction. This study demonstrated the practicality of ARM in co-morbidity studies using a large clinic database.
Authors:
A Mi Shin; In Hee Lee; Gyeong Ho Lee; Hee Joon Park; Hyung Seop Park; Kyung Il Yoon; Jung Jeung Lee; Yoon Nyun Kim
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Publication Detail:
Type:  Journal Article     Date:  2010-06-30
Journal Detail:
Title:  Healthcare informatics research     Volume:  16     ISSN:  2093-369X     ISO Abbreviation:  Healthc Inform Res     Publication Date:  2010 Jun 
Date Detail:
Created Date:  2011-08-05     Completed Date:  2011-11-10     Revised Date:  2013-05-29    
Medline Journal Info:
Nlm Unique ID:  101534553     Medline TA:  Healthc Inform Res     Country:  Korea (South)    
Other Details:
Languages:  eng     Pagination:  77-81     Citation Subset:  -    
Affiliation:
Department of Medical Informatics, School of Medicine, Keimyung University, Daegu, Korea.
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