Neural Networks Applied to Medical Data for Prediction of Patient Outcome

Prediction is vital in clinical fields, because it influences decision making for treatment and resource allocation. At present, medical records are readily accessible from hospital information systems. Based on the analysis of medical records, a number of predictive models have been developed to support the prediction of patient outcome. However, predictive models that achieve the desired predictive performance are few and far between.

In this chapter, we describe the capability of NNs applied to medical data for the prediction of patient outcome. Firstly, we applied a simple three-layer backpropagation NN to a dataset of intensive care unit (ICU) patients [12, 13] to develop a predictive model that estimates the probability of nosocomial infection. The predictive performance of the NN was compared with that of logistic regression using the cross-validation method.

Secondly, we invented a method of modeling time sequence data for prediction using multiple NNs. Based on the dataset of ICU patients, we examined whether multiple NNs outperform both logistic regression and the application of a single NN in the long-term prediction of nosocomial infection. According to the results of these studies, careful preparation of datasets improves the predictive performance of NNs, and accordingly, NNs outperform multivariate regression models. It is certain that NNs have capabilities as good predictive models. Further studies using real medical data may be required to achieve the desired predictive performance.