Data_Sheet_1_Machine learning algorithms assist early evaluation of enteral nutrition in ICU patients.docx

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.

研究背景
本研究应用机器学习（Machine Learning, ML）算法构建模型，用于预测重症监护病房（Intensive Care Unit, ICU）患者的肠内营养（Enteral Nutrition, EN）启动情况，并早期识别需要肠内营养的人群。

研究方法
本研究从重症监护医疗信息集市IV（Medical Information Mart for Intensive Care IV, MIMIC-IV）数据库中收集患者信息。所有纳入研究的患者被随机划分为训练集与验证集。本研究构建了6种机器学习模型以评估肠内营养启动情况，并根据曲线下面积（Area Under Curve, AUC）与准确率选出最优模型。随后采用局部可解释模型无关解释（Local Interpretable Model-Agnostic Explanations, LIME）算法与SHapley可加解释（SHapley Additive exPlanations, SHAP）值对最优模型进行解释。

研究结果
本研究共纳入53150例患者，其中训练集42520例（占比80%），验证集10630例（占比20%）。在验证集中，XGBoost模型展现出最优的预测性能，曲线下面积达0.895。SHAP值分析显示，脓毒症、序贯器官衰竭评估评分与急性肾损伤是影响肠内营养启动的三大关键因素。本研究通过LIME算法展示了个体化预测结果。

研究结论
本研究构建并验证了XGBoost模型，可用于ICU患者肠内营养启动的早期预测。