TY - JOUR
T1 - Constructing Inpatient Pressure Injury Prediction Models Using Machine Learning Techniques
AU - Hu, Ya Han
AU - Lee, Yi Lien
AU - Kang, Ming Feng
AU - Lee, Pei Ju
N1 - Publisher Copyright:
© Lippincott Williams & Wilkins.
PY - 2020
Y1 - 2020
N2 - The incidence rate of pressure injury is a critical nursing quality indicator in clinic care; consequently, factors causing pressure injury are diverse and complex. The early prevention of pressure injury and monitoring of these complex high-risk factors are critical to reduce the patients' pain, prevent further surgical treatment, avoid prolonged hospital stay, decrease the risk of wound infection, and lower associated medical costs and expenses. Although a number of risk assessment scales of pressure injury have been adopted in various countries, their criteria are set for specific populations, which may not be suitable for the medical care systems of other countries. This study constructs three prediction models of inpatient pressure injury using machine learning techniques, including decision tree, logistic regression, and random forest. A total of 11 838 inpatient records were collected, and 30 sets of training samples were adopted for data analysis in the experiment. The experimental results and evaluations of the models suggest that the prediction model built using random forest had most favorable classification performance of 0.845. The critical risk factors for pressure injury identified in this study were skin integrity, systolic blood pressure, expression ability, capillary refill time, and level of consciousness.
AB - The incidence rate of pressure injury is a critical nursing quality indicator in clinic care; consequently, factors causing pressure injury are diverse and complex. The early prevention of pressure injury and monitoring of these complex high-risk factors are critical to reduce the patients' pain, prevent further surgical treatment, avoid prolonged hospital stay, decrease the risk of wound infection, and lower associated medical costs and expenses. Although a number of risk assessment scales of pressure injury have been adopted in various countries, their criteria are set for specific populations, which may not be suitable for the medical care systems of other countries. This study constructs three prediction models of inpatient pressure injury using machine learning techniques, including decision tree, logistic regression, and random forest. A total of 11 838 inpatient records were collected, and 30 sets of training samples were adopted for data analysis in the experiment. The experimental results and evaluations of the models suggest that the prediction model built using random forest had most favorable classification performance of 0.845. The critical risk factors for pressure injury identified in this study were skin integrity, systolic blood pressure, expression ability, capillary refill time, and level of consciousness.
KW - Classification technique
KW - Inpatient pressure injury
KW - Machine learning
KW - Pressure injury risk assessment
UR - http://www.scopus.com/inward/record.url?scp=85089301997&partnerID=8YFLogxK
U2 - 10.1097/CIN.0000000000000604
DO - 10.1097/CIN.0000000000000604
M3 - 期刊論文
C2 - 32205474
AN - SCOPUS:85089301997
SN - 1538-2931
VL - 38
SP - 415
EP - 423
JO - CIN - Computers Informatics Nursing
JF - CIN - Computers Informatics Nursing
IS - 8
ER -