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Artificial neural networks within medical decision support systems

Caleb, Praminda; Sharpe, Peter K.; Caleb-Solly, Praminda

Authors

Praminda Caleb

Peter K. Sharpe

Praminda Caleb-Solly



Abstract

Artificial neural networks offer a way to actively assimilate both past and present knowledge, to extract information, to map correlations and to produce inferences from available data; all tasks which have relevance to the clinical laboratory. In this paper, we describe one useful artificial neural network technique, backpropagation, and describe some of the practical considerations which need to be taken account of when using such methods. Examples are presented of the application of artificial neural networks in medicine and, particularly, in clinical chemistry. The paper goes on to describe the use of these methods within medical decision support. We conclude that artificial neural networks are useful multivariate techniques which are well able to play an important role in a decision support system. Further, that their properties as function approximators could be utilised in other areas of clinical chemistry. We conclude by pointing out that the pattern recognition ability of artificial neural networks holds out the promise of extracting useful information from currently available data which is at present seen as being of little diagnostic utility. © 1994 Informa UK Ltd All rights reserved: reproduction in whole or part not permitted.

Journal Article Type Article
Publication Date Jan 1, 1994
Journal Scandinavian Journal of Clinical and Laboratory Investigation
Print ISSN 0036-5513
Electronic ISSN 1502-7686
Publisher Taylor & Francis (Routledge)
Peer Reviewed Peer Reviewed
Volume 54
Issue S219
Pages 3-11
DOI https://doi.org/10.3109/00365519409088571
Keywords artificial neural networks, medical decision support systems
Public URL https://uwe-repository.worktribe.com/output/1108971
Publisher URL http://dx.doi.org/10.3109/00365519409088571