Mixed neural-conventional processing to differentiate airway diseases by means of functional noninvasive tests

Author

Parvis, Marco ; Gulotta, Carlo ; Torchio, Roberto

Author_Institution

Dipt. di Electron., Torino Univ., Italy

Volume

50

Issue

3

fYear

2001

fDate

6/1/2001 12:00:00 AM

Firstpage

819

Lastpage

824

Abstract

This paper describes a processing technique that can be used to combine information from different medical analyses to discriminate between different pathologies that have similar symptoms. The paper is focused on the differentiation between asthma, bronchitis, and emphysema, using only functional noninvasive tests, but the proposed technique can be easily applied to other similar situations where different tests have to be used to identify a pathology. The technique is based on mixed neural-and-conventional processing that not only suggests the pathology, but also estimates the reliability of this suggestion

Keywords

Bayes methods; backpropagation; covariance matrices; diseases; error statistics; lung; medical diagnostic computing; multilayer perceptrons; pneumodynamics; Bayesian approach; airway diseases differentiation; asthma; backpropagation; bronchitis; competitive layer; covariance matrix; emphysema; error probability; functional noninvasive tests; linear discriminant score; lung pathologies discrimination; mixed neural-conventional processing; multilayer perceptron; pathology evidence index; spirometric data; uncertainty; Diseases; Information analysis; Lungs; Medical diagnostic imaging; Medical tests; Neural networks; Pathology; Patient monitoring; Testing; Uncertainty;

fLanguage

English

Journal_Title

Instrumentation and Measurement, IEEE Transactions on

Publisher

ieee

ISSN

0018-9456

Type

jour

DOI

10.1109/19.930460

Filename

930460