Modular Neural Networks for Medical Prognosis: Quantifying the Benefits of Combining Neural Networks for Survival Prediction
Lucila Ohno-Machado, Mark A. Musen Section on Medical Informatics Stanford University MSOB X-215 Stanford, CA 94305-5479 USA
Lucila Ohno-Machado is currently at: Decision Systems Group Brigham and Women’s Hospital/Harvard Medical School 75 Francis Street Boston, MA 02115 USA FAX (617)739-3672
[email protected]
keywords: modular neural networks, prognosis, survival an alysis
running head: Modular Networks for Prognosis
Abstract This paper describes a medical application of modular neural networks for temporal pattern recognition. In order to increase the reliability of prognostic indices for patients living with the Acquired Immunodeficiency Syndrome (AIDS), survival prediction was performed in a system composed of modular neural networks that classified cases according to death in a certain year of follow-up. The output of each neural network module corresponded to the probability of survival in a given year. Inputs were the values of demographic, clinical, and laboratory variables. The results of the modules were combined to produce survival curves for individuals. The neural networks were trained by backprogation and the results were evaluated in test sets of previously unseen cases. We showed that, for certain combinations of neural network modules, the performance of the prognostic index, measured by the area under the receiver operating characteristic (ROC) curve, was significantly improved (p
