Integrating artificial neural network and scoring systems to increase the prediction accuracy of patient mortality and organ dysfunction

Abstract

AbstractThe aim of this study was to develop and compare techniques to increase the prediction accuracy of patient mortality and organ dysfunction in the Intensive Care Units (hereinafter ICU) of hospitals. Patient mortality was estimated with two models of artificial neural network (ANN)-backpropagation (BP) and simplified acute physiology score (SAPS). Organ dysfunction was predicted by coupled ANN self-organizing map (SOM) and logistic organ dysfunction score (LODS) method on the basis of patient conditions. Input dataset consisted of 36 features recorded for 4,000 patients in the ICU. An integrated response surface methodology (RSM) and genetic algorithm (GA) was developed to achieve the best topology of the ANN-BP model. Although mortality prediction of the best ANN-BP (MSE = 0.0036, AUC = 0.83, R2 = 0.81) was more accurate than that of the SAPS score model (MSE = 0.0056, AUC = 0.82, R2 = 0.78), the execution time of the former (=45 min) was longer than that of the latter (=20 min). Therefore, the principal component analysis (PCA) was used to reduce the input feature dimensions, which, in turn, reduced the execution time up to 50%. Data reduction also helped to increase the network accuracy up to 90%. The likelihood of organ dysfunction determined by coupled ANN and scoring method technique can be much more efficient than the LODS model alone because the SOM could successfully classify the patients in 64 classes. The primary patient classification plays a major role in increasing the efficiency of an estimator.