Integrated Learning Model-Based Assessment of Enteral Nutrition Support in Neurosurgical Intensive Care Patients

Abstract

To observe the clinical efficacy of early enteral nutrition application in critically ill neurosurgical patients, in this paper, we have developed a prediction model for enteral nutrition support in neurosurgical intensive care patients which is primarily based on an integrated learning algorithm. Additionally, we have compared the prediction performance of each model. The patients were divided into control and combined treatment groups according to the random number table method, and 175 patients in each group were treated with a parenteral method and early enteral nutrition support, respectively. A reentry ICU prediction model based on the integrated learning method random forest, adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT) was developed, and the prediction performance of integrated learning and logistic regression was compared. The average sensitivity, positive predictive value, negative predictive value, false-positive rate, false-negative rate, area under the receiver operating characteristic curve (AUROC), and Brier score after fivefold cross-validation were used to evaluate model effects, and the best performance model based on the top 10 predictor variables in order of importance was given. Among all models, GBDT (AUROC=0.858) was better than random forest (AUROC=0.827) and slightly better than AdaBoost (AUROC=0.851). The GBDT algorithm gave a higher ranking of importance for variables such as mean arterial pressure, systolic blood pressure, diastolic blood pressure, heart rate, urine volume, and blood creatinine and relatively poorer cardiovascular and renal function in neurosurgical intensive care patients.