Artificial neural networks within medical decision support systems

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

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.

Citation

Caleb, P., Sharpe, P. K., & Caleb-Solly, P. (1994). Artificial neural networks within medical decision support systems. Scandinavian Journal of Clinical and Laboratory Investigation, 54(S219), 3-11. https://doi.org/10.3109/00365519409088571

• Journal Article Type: Article
• Publication Date: Jan 1, 1994
• Journal: Scandinavian Journal of Clinical and Laboratory Investigation
• Print ISSN: 0036-5513
• 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