TY - JOUR
T1 - Applying of decision tree analysis to risk factors associated with pressure ulcers in long-term care facilities
AU - Moon, Mikyung
AU - Lee, Soo Kyoung
N1 - Publisher Copyright:
© 2017 The Korean Society of Medical Informatics.
PY - 2017/1
Y1 - 2017/1
N2 - 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.
AB - 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.
KW - Data Mining
KW - Decision Trees
KW - Long-Term Care
KW - Pressure Ulcer
KW - Risk Factors
UR - http://www.scopus.com/inward/record.url?scp=85012024666&partnerID=8YFLogxK
U2 - 10.4258/hir.2017.23.1.43
DO - 10.4258/hir.2017.23.1.43
M3 - Article
AN - SCOPUS:85012024666
SN - 2093-3681
VL - 23
SP - 43
EP - 52
JO - Healthcare Informatics Research
JF - Healthcare Informatics Research
IS - 1
ER -