Fig 1.
Illustration of integration of the MIMIC-II database in a Hadoop/RapidMiner computer cluster: data retrieval and preprocessing.
Table 1.
Attributes selected for modeling and feature selection (weighting).
Table 2.
Characteristics of intensive care units survivors and non-survivors.
Fig 2.
Basic process for automatic building, parameter optimization and evaluation of multiple predictive models as displayed in RapidMiner.
Fig 3.
Illustration of the ensemble learning methods as displayed in RapidMiner (Decision Stump, AdaBoost, Random Forest, Bagging, W-J48, Decision Tree, Naive Bayes, Stacking, Logistic Regression, Support Vector Machine).
Table 3.
AUPRC (Area Under the Precision Recall Curve) performance and feature selection.
Fig 4.
AUPRC curves for the 3 best models.
Random Forest (RF) in association with Backward Selection (BS) and 69 features (left), with Forward Selection (FS) and 8 features (middle) and Gini Selection (GS) and 5 features.