A prototype integrated decision support system for breast cancer oncology

Authors:
Paulo J. G. Lisboa;Ian H. Jarman;Terence A. Etchells;Phillip Ramsey
Affiliations:
School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK;School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK;School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK;School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

Citing 4
Cited 1

A review of evidence of health benefit from artificial neural networks in medical intervention

Neural Networks
The use of artificial neural networks in decision support in cancer: A systematic review

Neural Networks
A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer

Artificial Intelligence in Medicine
Orthogonal search-based rule extraction (OSRE) for trained neural networks: a practical and efficient approach

IEEE Transactions on Neural Networks

A unified approach to the extraction of rules from artificial neural networks and support vector machines

ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes an integrated clinical support system combining data entry and access with prognostic modelling, for use by the clinician, complemented by a patient information system tailored to the particular characteristics of the individual patient. The core of the system comprises a modelling methodology based on the PLANN-ARD neural network which combines risk staging with automatic rule generation to derive an explanation facility for the risk group allocation of each patient. The aim of the system is to promote better informed decision making on the part of both the clinician and the patient, exploiting the combined potential of analytical methodologies and the internet.