Neural networks for measuring cancer outcomes

Harry B. Burke; Philip H. Goodman; David B. Rosen

Neural networks for measuring cancer outcomes

Harry B. Burke^*, Philip H. Goodman, David B. Rosen

^*Corresponding author for this work

Research output: Contribution to conference › Paper › peer-review

4 Scopus citations

Abstract

The purpose of this work is to present an evidence that cancer is a complex system, that future prognostic factors will be nonmonotonic and they will exhibit complex interactions. Because TNM staging systems do not give the desired accuracy, artificial neural networks (ANNs) were considered. It was shown that ANNs can perform as well as the best traditional prediction methods, and they can capture the power of nonmonotonic predictors and discover complex interactions.

Original language	English
Pages	157-159
Number of pages	3
State	Published - 1994
Externally published	Yes
Event	Proceedings of the 1994 IEEE Instrumentation and Measurement Technology Conference. Part 2 (of 3) - Hamamatsu, Jpn Duration: 10 May 1994 → 12 May 1994

Conference

Conference	Proceedings of the 1994 IEEE Instrumentation and Measurement Technology Conference. Part 2 (of 3)
City	Hamamatsu, Jpn
Period	10/05/94 → 12/05/94

Cite this

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title = "Neural networks for measuring cancer outcomes",

abstract = "The purpose of this work is to present an evidence that cancer is a complex system, that future prognostic factors will be nonmonotonic and they will exhibit complex interactions. Because TNM staging systems do not give the desired accuracy, artificial neural networks (ANNs) were considered. It was shown that ANNs can perform as well as the best traditional prediction methods, and they can capture the power of nonmonotonic predictors and discover complex interactions.",

author = "Burke, {Harry B.} and Goodman, {Philip H.} and Rosen, {David B.}",

year = "1994",

language = "English",

pages = "157--159",

note = "Proceedings of the 1994 IEEE Instrumentation and Measurement Technology Conference. Part 2 (of 3) ; Conference date: 10-05-1994 Through 12-05-1994",

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TY - CONF

T1 - Neural networks for measuring cancer outcomes

AU - Burke, Harry B.

AU - Goodman, Philip H.

AU - Rosen, David B.

PY - 1994

Y1 - 1994

N2 - The purpose of this work is to present an evidence that cancer is a complex system, that future prognostic factors will be nonmonotonic and they will exhibit complex interactions. Because TNM staging systems do not give the desired accuracy, artificial neural networks (ANNs) were considered. It was shown that ANNs can perform as well as the best traditional prediction methods, and they can capture the power of nonmonotonic predictors and discover complex interactions.

AB - The purpose of this work is to present an evidence that cancer is a complex system, that future prognostic factors will be nonmonotonic and they will exhibit complex interactions. Because TNM staging systems do not give the desired accuracy, artificial neural networks (ANNs) were considered. It was shown that ANNs can perform as well as the best traditional prediction methods, and they can capture the power of nonmonotonic predictors and discover complex interactions.

UR - http://www.scopus.com/inward/record.url?scp=0028753994&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:0028753994

SP - 157

EP - 159

T2 - Proceedings of the 1994 IEEE Instrumentation and Measurement Technology Conference. Part 2 (of 3)

Y2 - 10 May 1994 through 12 May 1994

ER -

Neural networks for measuring cancer outcomes

Abstract

Conference

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Cite this