Abstract:

Objective To construct prediction models for the risk of neonatal pressure injury using machine learning，and compare the performance of the models，in order to provide insights for predicting，early prevention and treatment of neonatal pressure injury. Methods Clinical data of neonates from a tertiary class A hospital in Zhejiang Province were collected and randomly divided into a training set and a validation set according to a ratio of 8：2. The main outcome was whether pressure injury occurred. Through literature review and risk factor analysis，14 common clinical features were selected as predictors，and four machine learning algorithms，logistic regression，random forest，support vector machine and decision tree，were used to construct prediction models for the risk of neonatal pressure injury. The model was tested and calibrated by five-fold cross validation，and the model performance was evaluated and compared by accuracy，precision，recall，F score and area under the receiver operating characteristic curve（ROC AUC）. Decision curve analysis（DCA） was used to evaluate the clinical utility of the models，and the importance of the features was displayed by SHAP. Results A total of 1 642 neonates were included，251 of which developed pressure injury（15.3%）. All four prediction models had good predictive performance，and the AUC values of logistic regression，random forest，support vector machine and decision tree models were 0.85，0.88，0.90 and 0.84，respectively. The accuracy rates were 0.78，0.80，0.83 and 0.79，respectively. Accuracy were 0.72，0.75，0.77，0.73. The recall rates were 0.91，0.93，0.94，0.91，respectively. The F scores were 0.80，0.83，0.85 and 0.81，respectively. DCA showed that under the threshold probability of 0.1-0.4，the random forest model had the highest net benefit. The use of non-invasive respiratory support，gestational age at birth，and birth weight were the top three most important clinical characteristic variables for the SVM and random forest models. Conclusion Machine learning can be used to construct the risk prediction models of neonatal pressure injury. The model based on random forest is the best considering both on the performance and clinical utility.

Key words: Machine Learning, Neonatal, Pressure Injury, Predictive Model