Artificial neural networks within medical decision support systems

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.