Healthc Inform Res.  2017 Jan;23(1):43-52. 10.4258/hir.2017.23.1.43.

Applying of Decision Tree Analysis to Risk Factors Associated with Pressure Ulcers in Long-Term Care Facilities

• Moon M
• Lee SK

• KMID: 2368984
• DOI: http://doi.org/10.4258/hir.2017.23.1.43

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.

Keyword

Data Mining; Decision Trees; Long-Term Care; Pressure Ulcer; Risk Factors

MeSH Terms

Aged
Bandages
Classification
Comorbidity
Data Mining
Decision Trees*
Diagnosis
Hip
Hospital Costs
Humans
Inpatients
Insurance, Health
Length of Stay
Long-Term Care*
Machine Learning
Pressure Ulcer*
Prevalence
Risk Factors*
Sensitivity and Specificity
Suppuration
Wounds and Injuries

Figure

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  Figure 1 Flow chart of data management.

• 

  Figure 2 Sample selection process from 2014 Health Insurance Review and Assessment Service National Inpatient Sample (HIRA NIS).

• 

  Figure 3 Boxplot of the 10-fold cross-validation results.

• 

  Figure 4 Decision tree.

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