Objective. To compare the abilities of artificial neural network and logistic regression models
to predict the risk of in-hospital mortality after coronary artery bypass graft (CABG) surgery …

Objective. To develop a probabilistic neural network (PNN) to estimate mortality risk
following cardiac surgery. Design and setting. The PNN model was created using an …

Background. Neural networks are nonparametric, robust, pattern recognition techniques that
can be used to model complex relationships. Methods. The applicability of multilayer …

OBJECTIVE: The artificial neural network model is a nonlinear technology useful for
complex pattern recognition problems. This study aimed to develop a method to select risk …

Neural networks were developed to predict perioperative cardiac complications with data
from 567 vascular surgery patients. Neural network scores were based on cardiac risk …

Experiments demonstrated that sigmoid multilayer perceptron (MLP) networks provide
slightly better risk prediction than conventional logistic regression when used to predict the …

Risk-adjusted mortality prediction is frequently used to assess the outcome of coronary
artery bypass grafting (CABG). By taking into account patient risk factors, this allows more …

We constructed and internally validated an artificial neural network (ANN) model for
prediction of in-hospital major adverse outcomes (defined as death, cardiac arrest, coma …

Background. In the study presented here, an artificial neural network was used to “learn” the
relationship between 11 risk factors and patient surgical outcome (survival or death). The …

OBJECTIVES To perform a systematic comparison of in-hospital mortality risk prediction post-
cardiac surgery, between the predominant scoring system—European System for Cardiac …