A Comparison of Intensive Care Unit Mortality Prediction Models through the Use of Data Mining Techniques

Abstract

Objectives: The intensive care environment generates a wealth of critical care data suited to developing a well-calibrated prediction tool. This study was done to develop an intensive care unit (ICU) mortality prediction model built on University of Kentucky Hospital (UKH)'s data and to assess whether the performance of various data mining techniques, such as the artificial neural network (ANN), support vector machine (SVM) and decision trees (DT), outperform the conventional logistic regression (LR) statistical model.

Methods: The models were built on ICU data collected regarding 38,474 admissions to the UKH between January 1998 and September 2007. The first 24 hours of the ICU admission data were used, including patient demographics, admission information, physiology data, chronic health items, and outcome information.

Results: Only 15 study variables were identified as significant for inclusion in the model development. The DT algorithm slightly outperformed (AUC, 0.892) the other data mining techniques, followed by the ANN (AUC, 0.874), and SVM (AUC, 0.876), compared to that of the APACHE III performance (AUC, 0.871).

Conclusions: With fewer variables needed, the machine learning algorithms that we developed were proven to be as good as the conventional APACHE III prediction.

Keywords: APACHE; Decision Trees; Intensive Care Units; Neural Networks; Support Vector Machines.

Figure 1

Receiver operating characteristic (ROC) results of prediction models developed. ANN: artificial neural network, SVM: support vector machine.

Figure 2

Abridged decision trees (DT) graph. GCS: Glasgow Coma Score, ABG: arterial blood gases.