Applying of Decision Tree Analysis to Risk Factors Associated with Pressure Ulcers in Long-Term Care Facilities.
- Author:
Mikyung MOON
1
;
Soo Kyoung LEE
Author Information
- Publication Type:Original Article
- Keywords: Data Mining; Decision Trees; Long-Term Care; Pressure Ulcer; Risk Factors
- MeSH: 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
- From:Healthcare Informatics Research 2017;23(1):43-52
- CountryRepublic of Korea
- Language:English
- 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.
