Constructing Inpatient Pressure Injury Prediction Models Using Machine Learning Techniques

Ya Han Hu, Yi Lien Lee, Ming Feng Kang, Pei Ju Lee

研究成果: 雜誌貢獻期刊論文同行評審

15 引文 斯高帕斯(Scopus)


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.

頁(從 - 到)415-423
期刊CIN - Computers Informatics Nursing
出版狀態已出版 - 2020


深入研究「Constructing Inpatient Pressure Injury Prediction Models Using Machine Learning Techniques」主題。共同形成了獨特的指紋。