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.

Gastrointestinal failure, big data and intensive care

“…In a recent study, involving neurosurgical patients, 350 patients were randomized to receive enteral feeding only or parenteral nutrition and low-energy enteral feeding administered progressively. A prediction model of enteral nutrition support for severe neurosurgical patients was established based on an integrated learning algorithm, and the prediction performance of each model was compared [34]. Our group used a supervised machine learning approach to examine EFI along the early acute phase as a predictor of clinical outcomes (including ICU mortality, hospital mortality, 28-day mortality, 90-day mortality and length of stay greater than 7 days) in critically ill patients [35].…”

Retracted: Integrated Learning Model‐Based Assessment of Enteral Nutrition Support in Neurosurgical Intensive Care Patients

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”