Bootstrapping Confidence Intervals for Clinical Input Variable Effects in a Network Trained to Identify the Presence of Acute Myocardial Infarction

The artificial neural network has been successfully applied to a broad range of clinical settings (Widrow and Hoff 1960; Rumelhart et al. 1986; McClelland et al. 1988; Weigend et al. 1990; Hudson et al. 1988; Smith et al. 1988; Saito and Nakano 1988; Kaufman et al. 1990; Hiraiwa et al. 1990; Cios et al. 1990; Marconi et al. 1989; Eberhard et al. 1991; Mulsant and Servan-Schreiber 1988; Bounds et al. 1990; Yoon et al. 1989). Such a network has been adapted for use as an aid to the clinical diagnosis of acute myocardial infarction (Baxt 1990, 1991, 1992a; Harrison et al. 1991) (heart attack). Both initial retrospective and subsequent prospective studies have revealed that this network performed more accurately than either physicians or other electronic data processing technologies (Baxt 1990, 1991; Goldman et al. 1988). Since nonlinear artificial networks are known to be capable of identifying relationships between input data that are not apparent to human analysis (Weigend et al. 1990), one hope has been that the network could be utilized to identify relationships in clinical data that have not been revealed by previous study. The inherent problem in this hope has been the inability easily to identify how artificial neural networks derive their output. One indirect way that this can be approached is by the stepwise perturbation of isolated individual input variables across a large number of patterns coupled with an analysis of the effect this has on network output. Prior application of this analysis to the artificial neural network trained to identify the presence of acute myocardial infarction revealed that one could gain a

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