Hierarchical Neural Networks for Partial Diagnosis in Medicine

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Citation: Lucila Ohno-Machado and Mark A. Musen. (1993) Hierarchical Neural Networks for Partial Diagnosis in Medicine. In KSL-93-68, December,1993.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Lucila Ohno-Machado and Mark A. Musen
title Hierarchical Neural Networks for Partial Diagnosis in Medicine
number KSL-93-68
institution Knowledge Systems, AI Laboratory
year 1993
month December
Bibtex more
note Medical Computer Science
Access Paper
abstract Various domains require hierarchical classification. In medicine, learning partial diagnoses can be helpful when time and information constraints are present. Hierarchical neural networks provide a good means to perform partial diagnosis. We implemented a hierarchical backpropagation-based model for the domain of thyroid diseases, and compared the results against those of nonhierarchical networks in terms of sensitivities and specificities. In our system, high-level neural networks filter instances that are relevant for use in specialized neural networks. The hierarchical model required fewer epochs to be trained and yielded a higher classification rate in the test set than did the nonhierarchical one. The hierarchical model also had the advantage that fewer data attributes for each instance were required at higher levels. Therefore, using this model decreases the problem of dealing with missing values, and provides a framework to establish a parsimonious sequence of tests for diagnosing thyroid diseases.

KSL Technical Report ID: KSL-93-68
Facts about Hierarchical Neural Networks for Partial Diagnosis in MedicineRDF feed
Abstract Various domains require hierarchical class … Various domains require hierarchical classification. In medicine, learning partial diagnoses can be helpful when time and information constraints are present. Hierarchical neural networks provide a good means to perform partial diagnosis. We implemented a hierarchical backpropagation-based model for the domain of thyroid diseases, and compared the results against those of nonhierarchical networks in terms of sensitivities and specificities. In our system, high-level neural networks filter instances that are relevant for use in specialized neural networks. The hierarchical model required fewer epochs to be trained and yielded a higher classification rate in the test set than did the nonhierarchical one. The hierarchical model also had the advantage that fewer data attributes for each instance were required at higher levels. Therefore, using this model decreases the problem of dealing with missing values, and provides a framework to establish a parsimonious sequence of tests for diagnosing thyroid diseases. of tests for diagnosing thyroid diseases.
Author Lucila Ohno-Machado and Mark A. Musen  +
Bibtype techreport  +
Has author Lucila Ohno-Machado and Mark A. Musen  +
Has identifier KSL-93-68  +
Has publishing details December,1993  +
Has title Hierarchical Neural Networks for Partial Diagnosis in Medicine  +
Has where published KSL-93-68  +
Has year 1993  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-93-68  +
Month December  +
Note Medical Computer Science
Number KSL-93-68  +
Process note NO  +
Title Hierarchical Neural Networks for Partial Diagnosis in Medicine  +
Year 1993  +
Retrieved from "http://tw.rpi.edu/wiki/Hierarchical_Neural_Networks_for_Partial_Diagnosis_in_Medicine"
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