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Medical Applications of Neural Networks: Connectionist Models of Survival

Although neural networks have been applied to medical problems in recent years, their applicability has been limited for a variety of reasons. One of those barriers has been the problem of recognizing rare categories. In this dissertation, I demonstrate, and prove the utility of, a new method for tackling this problem. In particular, I have developed a method that allows the recognition of rare categories with high sensitivity and specificity, and will show that it is practical and robust. This method involves the construction of sequential neural networks. Rare categories occur and must be learned if practical application of neural-network technology is to be achieved. Survival analysis is one area in which this problem appears. In this work, I test the hypotheses that (1) sequential systems of neural networks produce results that are more accurate (in terms of calibration and resolution) than nonsequential neural networks; and (2) in certain circumstances, sequential neural networks produce more accurate estimates of survival time than Cox proportional hazards and logistic regression models. I use two sets of data to test the hypotheses: (1) a data set of HIV+ patients; and (2) a data set of patients followed prospectively for the development of cardiac conditions. I show that a neural network model can predict death due to AIDS more accurately than a Cox proportional hazards model. Furthermore, I show that a sequential neural network model is more accurate than a standard neural network model. I show that the predictions of logistic regression and neural networks are not significantly different, but that any of these models used sequentially is more accurate than its standard counterpart.