A neural network trained to identify the presence of myocardial infarction bases some decisions on clinical associations that differ from accepted clinical teaching.

An artificial neural network trained to identify the presence of myocardial infarction has been shown to function with a high degree of accuracy. The effects on network diagnosis of some of the clinical input variables used by this network have previously been shown to be distributed over two distinct maxima. Analysis of the basis for this distribution by studying the specific patterns in which these variables had significantly different impacts on network diagnosis revealed that the differential impacts were due to the contexts in which the variables whose effects were bimodally distributed were placed. These contexts were defined by the values of the other input data used by the network. In a number of instances, the clinical relationships implied by these associations were divergent from prior knowledge about factors predictive of myocardial infarction. One implication of these findings is that this network, which has been shown to perform with a high degree of diagnostic accuracy, may be doing so by identifying relationships between inputted information that are divergent from accepted teaching.