Machine learning algorithms assist early evaluation of enteral nutrition in ICU patients

BackgroundThis study applied machine learning (ML) algorithms to construct a model for predicting EN initiation for patients in the intensive care unit (ICU) and identifying populations in need of EN at an early stage. MethodsThis study collected patient information from the Medical Information Mart for Intensive Care IV database. All patients enrolled were split randomly into a training set and a validation set. Six ML models were established to evaluate the initiation of EN, and the best model was determined according to the area under curve (AUC) and accuracy. The best model was interpreted using the Local Interpretable Model-Agnostic Explanations (LIME) algorithm and SHapley Additive exPlanation (SHAP) values. ResultsA total of 53,150 patients participated in the study. They were divided into a training set (42,520, 80%) and a validation set (10,630, 20%). In the validation set, XGBoost had the optimal prediction performance with an AUC of 0.895. The SHAP values revealed that sepsis, sequential organ failure assessment score, and acute kidney injury were the three most important factors affecting EN initiation. The individualized forecasts were displayed using the LIME algorithm. ConclusionThe XGBoost model was established and validated for early prediction of EN initiation in ICU patients.

Automated summary

This study developed a machine learning model, specifically the XGBoost model, to predict the initiation of enteral nutrition (EN) in ICU patients. The model performed well in predicting the need for early initiation of EN.