DESKNET: The dermatology expert system with knowledge-based network

Youngohc Yoon; Lynn L. Peterson; Paul R. Bergstresser

doi:10.1016/0893-6080(88)90501-1

DESKNET: The dermatology expert system with knowledge-based network

Youngohc Yoon, Lynn L. Peterson, Paul R. Bergstresser

Dermatology

Research output: Contribution to journal › Conference article › peer-review

2 Scopus citations

Abstract

A prototypical connectionist expert system is presented. DESKNET is built for ultimate use in the instruction of medical students to diagnose papulosquamous skin diseases. DESKNET is an ES composed of a three layered back propagation network consisting of an input layer, a hidden layer, and an output layer. The units on a layer have excitatory or inhibitory connections to units on the adjacent layers. Input for the system is a list of symptoms organized by 18 parameters. The back propagation algorithm encodes the implicit 'knowledge' such that it remains in an implicit form; thus, more knowledge can be captured. Therefore, the back propagation model appears to be a useful tool for representing concepts of a domain and encoding them in a network. Limitations of the algorithm are identified.

Original language	English (US)
Pages (from-to)	477
Number of pages	1
Journal	Neural Networks
Volume	1
Issue number	1 SUPPL
DOIs	https://doi.org/10.1016/0893-6080(88)90501-1
State	Published - 1988
Event	International Neural Network Society 1988 First Annual Meeting - Boston, MA, USA Duration: Sep 6 1988 → Sep 10 1988

ASJC Scopus subject areas

Cognitive Neuroscience
Artificial Intelligence

Access to Document

10.1016/0893-6080(88)90501-1

Cite this

@article{ce5626abaf5b4cbb824e222d49f7390a,

title = "DESKNET: The dermatology expert system with knowledge-based network",

abstract = "A prototypical connectionist expert system is presented. DESKNET is built for ultimate use in the instruction of medical students to diagnose papulosquamous skin diseases. DESKNET is an ES composed of a three layered back propagation network consisting of an input layer, a hidden layer, and an output layer. The units on a layer have excitatory or inhibitory connections to units on the adjacent layers. Input for the system is a list of symptoms organized by 18 parameters. The back propagation algorithm encodes the implicit 'knowledge' such that it remains in an implicit form; thus, more knowledge can be captured. Therefore, the back propagation model appears to be a useful tool for representing concepts of a domain and encoding them in a network. Limitations of the algorithm are identified.",

author = "Youngohc Yoon and Peterson, {Lynn L.} and Bergstresser, {Paul R.}",

year = "1988",

doi = "10.1016/0893-6080(88)90501-1",

language = "English (US)",

volume = "1",

pages = "477",

journal = "Neural Networks",

issn = "0893-6080",

publisher = "Elsevier Limited",

number = "1 SUPPL",

note = "International Neural Network Society 1988 First Annual Meeting ; Conference date: 06-09-1988 Through 10-09-1988",

}

TY - JOUR

T1 - DESKNET

T2 - International Neural Network Society 1988 First Annual Meeting

AU - Yoon, Youngohc

AU - Peterson, Lynn L.

AU - Bergstresser, Paul R.

PY - 1988

Y1 - 1988

N2 - A prototypical connectionist expert system is presented. DESKNET is built for ultimate use in the instruction of medical students to diagnose papulosquamous skin diseases. DESKNET is an ES composed of a three layered back propagation network consisting of an input layer, a hidden layer, and an output layer. The units on a layer have excitatory or inhibitory connections to units on the adjacent layers. Input for the system is a list of symptoms organized by 18 parameters. The back propagation algorithm encodes the implicit 'knowledge' such that it remains in an implicit form; thus, more knowledge can be captured. Therefore, the back propagation model appears to be a useful tool for representing concepts of a domain and encoding them in a network. Limitations of the algorithm are identified.

AB - A prototypical connectionist expert system is presented. DESKNET is built for ultimate use in the instruction of medical students to diagnose papulosquamous skin diseases. DESKNET is an ES composed of a three layered back propagation network consisting of an input layer, a hidden layer, and an output layer. The units on a layer have excitatory or inhibitory connections to units on the adjacent layers. Input for the system is a list of symptoms organized by 18 parameters. The back propagation algorithm encodes the implicit 'knowledge' such that it remains in an implicit form; thus, more knowledge can be captured. Therefore, the back propagation model appears to be a useful tool for representing concepts of a domain and encoding them in a network. Limitations of the algorithm are identified.

UR - http://www.scopus.com/inward/record.url?scp=0024177529&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0024177529&partnerID=8YFLogxK

U2 - 10.1016/0893-6080(88)90501-1

DO - 10.1016/0893-6080(88)90501-1

M3 - Conference article

AN - SCOPUS:0024177529

SN - 0893-6080

VL - 1

SP - 477

JO - Neural Networks

JF - Neural Networks

IS - 1 SUPPL

Y2 - 6 September 1988 through 10 September 1988

ER -

DESKNET: The dermatology expert system with knowledge-based network

Abstract

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this