KSL-96-11 + redirect page
Modular Neural Networks for Medical Prognosis: Quantifying the Benefits of Combining Neural Networks for Survival Prediction + Has identifier
Modular Neural Networks for Medical Prognosis: Quantifying the Benefits of Combining Neural Networks for Survival Prediction + Ksl tr id
Modular Neural Networks for Medical Prognosis: Quantifying the Benefits of Combining Neural Networks for Survival Prediction + Number
| Modular Neural Networks for Medical Prognosis: Quantifying the Benefits of Combining Neural Networks for Survival Prediction |
Bibtype
techreport
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
Title
Modular Neural Networks for Medical Prognosis: Quantifying the Benefits of Combining Neural Networks for Survival Prediction
Year
1996
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.
Note
Medical Computer Science
Author
Lucila Ohno-Machado and Mark A. Musen +
Has author
Lucila Ohno-Machado and Mark A. Musen +
Has identifier
Modular Neural Networks for Medical Prognosis: Quantifying the Benefits of Combining Neural Networks for Survival Prediction +
Institution
Knowledge Systems, AI Laboratory +
Ksl tr id
Modular Neural Networks for Medical Prognosis: Quantifying the Benefits of Combining Neural Networks for Survival Prediction +
Month
February +
Number
Modular Neural Networks for Medical Prognosis: Quantifying the Benefits of Combining Neural Networks for Survival Prediction +
Process note
NO +
Categories KSL Technical Report +, Publication +, Technical Report +
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