TY - GEN
T1 - An artificial neural network to predict mortality in patients who undergo percutaneous coronary interventions
AU - Tourassi, Georgia D.
AU - Xenopoulos, Nicholas P.
PY - 1997
Y1 - 1997
N2 - The objective of this study was to develop a method for identifying patients at increased risk for mortality after percutaneous coronary interventions (PCI). Although the mortality rate after PCI is low (1-2%), the ability to predict the patients with increased risk of mortality can alter the preferred medical strategy and potentially improve the outcome of the patient. We developed a feedforward artificial neural network (ANN) which predicts mortality using 24 variables. The study was based on 812 consecutive patients who underwent PCI between 1.1.95 and 6.30.95 at the Jewish Hospital Heart and Lung Center, Louisville, KY. The predictive power of the network was compared to that of linear discriminant analysis (LDA) using receiver operating characteristics methodology. Our study showed that the performance of the network strongly depended on the choice of the criterion function. Specifically, a modified cross-entropy function worked the best for the network resulting in an ROC area index of Az(ANN)=0.84/spl plusmn/0.07 compared to Az(LDA)=0.64/spl plusmn/0.12.
AB - The objective of this study was to develop a method for identifying patients at increased risk for mortality after percutaneous coronary interventions (PCI). Although the mortality rate after PCI is low (1-2%), the ability to predict the patients with increased risk of mortality can alter the preferred medical strategy and potentially improve the outcome of the patient. We developed a feedforward artificial neural network (ANN) which predicts mortality using 24 variables. The study was based on 812 consecutive patients who underwent PCI between 1.1.95 and 6.30.95 at the Jewish Hospital Heart and Lung Center, Louisville, KY. The predictive power of the network was compared to that of linear discriminant analysis (LDA) using receiver operating characteristics methodology. Our study showed that the performance of the network strongly depended on the choice of the criterion function. Specifically, a modified cross-entropy function worked the best for the network resulting in an ROC area index of Az(ANN)=0.84/spl plusmn/0.07 compared to Az(LDA)=0.64/spl plusmn/0.12.
UR - http://www.scopus.com/inward/record.url?scp=0030651562&partnerID=8YFLogxK
U2 - 10.1109/ICNN.1997.614544
DO - 10.1109/ICNN.1997.614544
M3 - Conference contribution
AN - SCOPUS:0030651562
SN - 0780341228
SN - 9780780341227
T3 - IEEE International Conference on Neural Networks - Conference Proceedings
SP - 2464
EP - 2467
BT - 1997 IEEE International Conference on Neural Networks, ICNN 1997
T2 - 1997 IEEE International Conference on Neural Networks, ICNN 1997
Y2 - 9 June 1997 through 12 June 1997
ER -