Discussion
sepsis, death, Low cardiac output, hypoperfusion
VASODILATION, DTS, CARDIOGENIC SHOCK, HYPOPERFUSION, REGRESSION, SEPARATION, SEPSIS
The final analysis of this study included 1342 patients with ECMO support. Compared to conventional approaches, this was by far the largest study of patients on ECMO evaluating prognostic indicators using machine learning algorithms of RF. One of the most influential features in predicting future death, among all anthropometric, hemodynamic, and laboratory variables, was urine output during the first 24 h after ECMO placement.As part of the training process, RF uses many decision trees (DT) to determine a classifier. In both classification and regression tasks, the DT model is a non-parametric model based on a tree-like graph. A top-to-bottom tree-like structure represents the relationship between the recruiting features and the target variable. The DTs are very sensitive to the training data, so they are error-prone on the test data set. Different parts of the training data set are used for each DT to search for the most “votes,” which are combinations of features and thresholds which will result in the "most effective" separation between the two classes. Due to the fact that the RF algorithm interprets and gets the results from multiple DTs, it can reduce the variance that would result from considering just one DT alone [According to both RF and LR models (Fig. In addition, urine output was routinely monitored in the ICU. Thus, urine output is a noninvasive, easily accessible, inexpensive, and ideal parameter for detecting high-risk mortality patients receiving ECMO support in time and intervening before adverse clinical outcomes occur. Low cardiac output (cardiogenic shock), systemic vasodilation (sepsis), and organ hypoperfusion may account for the decreased urine production under ECMO [