Description

Abstract:

In this paper, propose an innovative data mining framework and apply it to mine potential causal associations in electronic patient data sets where the drug-related events of interest occur infrequently.  Specifically, created a novel interestingness measure, exclusive causal-leverage, based on a computational, fuzzy recognition-primed decision (RPD) model that we previously developed. On the basis of this new measure, a data mining algorithm was developed to mine the causal relationship between drugs and their associated adverse drug reactions (ADRs). The exclusive causal-leverage was employed to rank the potential causal associations between each of the selected drugs. Algorithm could effectively make known ADRs rank high among all the symptoms in the database.