Bayesian Neural Networks used to Find Adverse Drug Combinations and Drug Related Syndromes

Authors: Roland Orre, Andrew Bate and Marie Lindquist.

Abstract

The data mining task we are interrested in is to find associations between variables in a large database. The method we have earlier proposed to find outstanding associations is to compare estimated frequencies of combinations of variables with the frequencies that would be predicted assuming there were no dependencies. The method we now propose use the same strategy as an efficient way of finding complex dependencies, i.e. certain combinations of explanatory variables, mainly medical drugs, which may be highly associated with certain outcome events or combinations of adverse drug reactions (ADRs). Such combinations of ADRs may also be recognized as syndromes.

The method we use for data mining is an artificial neural network architecture denoted Bayesian Confidence Propagation Neural Network (BCPNN). To decide whether the joint probabilities of events are different from what would follow from the independence assumption, the "information component" log(P_ij /(P_iP_j)), which is a weight in the BCPNN, and its variance plays a crucial role. We also suggest how this method might be used in combination with stochastic~EM to analyse conditioned dependencies also between real valued variables, e.g. to consider the amount of each drug taken.