Modular Neural Networks for Medical Prognosis: Quantifying the Benefits of Combining Neural Networks for Survival Prediction

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Citation: Lucila Ohno-Machado and Mark A. Musen. (1996) Modular Neural Networks for Medical Prognosis: Quantifying the Benefits of Combining Neural Networks for Survival Prediction. In KSL-96-11, February,1996.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Lucila Ohno-Machado and Mark A. Musen
title Modular Neural Networks for Medical Prognosis: Quantifying the Benefits of Combining Neural Networks for Survival Prediction
number KSL-96-11
institution Knowledge Systems, AI Laboratory
year 1996
month February
Bibtex more
note Medical Computer Science
Access Paper
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 monotonic 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<0.05). We also used calibration measurements to quantify the benefits of combining neural network modules, and show why, when, and how neural networks should be combined for building prognostic models.

KSL Technical Report ID: KSL-96-11
Facts about Modular Neural Networks for Medical Prognosis: Quantifying the Benefits of Combining Neural Networks for Survival PredictionRDF feed
Abstract This paper describes a medical application … 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 monotonic 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<0.05). We also used calibration measurements to quantify the benefits of combining neural network modules, and show why, when, and how neural networks should be combined for building prognostic models. e combined for building prognostic models.
Author Lucila Ohno-Machado and Mark A. Musen  +
Bibtype techreport  +
Has author Lucila Ohno-Machado and Mark A. Musen  +
Has identifier KSL-96-11  +
Has publishing details February,1996  +
Has title Modular Neural Networks for Medical Prognosis: Quantifying the Benefits of Combining Neural Networks for Survival Prediction  +
Has where published KSL-96-11  +
Has year 1996  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-96-11  +
Month February  +
Note Medical Computer Science
Number KSL-96-11  +
Process note NO  +
Title Modular Neural Networks for Medical Prognosis: Quantifying the Benefits of Combining Neural Networks for Survival Prediction  +
Year 1996  +
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