Constructing Inpatient Pressure Injury Prediction Models Using Machine Learning Techniques

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

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


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.

Original languageEnglish
Pages (from-to)415-423
Number of pages9
JournalCIN - Computers Informatics Nursing
Issue number8
StatePublished - 2020


  • Classification technique
  • Inpatient pressure injury
  • Machine learning
  • Pressure injury risk assessment


Dive into the research topics of 'Constructing Inpatient Pressure Injury Prediction Models Using Machine Learning Techniques'. Together they form a unique fingerprint.

Cite this