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Applying of decision tree analysis to risk factors associated with pressure ulcers in long-term care facilities

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Author(s)
이수경
Alternative Author(s)
Lee, Soo Kyoung
Publication Year
2017
Keyword
Data MiningDecision TreesLong-Term CareRisk FactorsPressure Ulcer
Abstract
Objectives: The purpose of this study was to use decision tree analysis to explore the factors associated with pressure ulcers
(PUs) among elderly people admitted to Korean long-term care facilities. Methods: The data were extracted from the 2014
National Inpatient Sample (NIS)—data of Health Insurance Review and Assessment Service (HIRA). A MapReduce-based
program was implemented to join and filter 5 tables of the NIS. The outcome predicted by the decision tree model was the
prevalence of PUs as defined by the Korean Standard Classification of Disease-7 (KCD-7; code L89*). Using R 3.3.1, a decision
tree was generated with the finalized 15,856 cases and 830 variables. Results: The decision tree displayed 15 subgroups
with 8 variables showing 0.804 accuracy, 0.820 sensitivity, and 0.787 specificity. The most significant primary predictor of PUs
was length of stay less than 0.5 day. Other predictors were the presence of an infectious wound dressing, followed by having
diagnoses numbering less than 3.5 and the presence of a simple dressing. Among diagnoses, “injuries to the hip and thigh”
was the top predictor ranking 5th overall. Total hospital cost exceeding 2,200,000 Korean won (US $2,000) rounded out the
top 7. Conclusions: These results support previous studies that showed length of stay, comorbidity, and total hospital cost
were associated with PUs. Moreover, wound dressings were commonly used to treat PUs. They also show that machine learning,
such as a decision tree, could effectively predict PUs using big data.
Department
Dept. of Nursing (간호학)
Publisher
College of Nursing
Citation
Healthcare Informatics Research, Vol.23(1) : 43-52, 2017
Type
Article
ISSN
2093-3681
DOI
10.4258/hir.2017.23.1.43
URI
http://kumel.medlib.dsmc.or.kr/handle/2015.oak/32413
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