Mining Infrequent Causal Associations in Electronic Health Databases

Discovering infrequent causal relationships can help us prevent or correct negative outcomes caused by their antecedents. In this paper, we propose an innovative data mining framework and apply it to mine potential causal associations in electronic patient datasets where the drug-related events of interest occur infrequently. Specifically, we 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 algorithm was tested on real patient data retrieved from the Veterans Affairs Medical Center in Detroit, Michigan. The exclusive causal-leverage was employed to rank the potential causal associations between each of the two selected drugs (i.e., enalapril and pravastatin) and 3,954 recorded symptoms, each of which corresponds to a potential ADR. The top 10 drug-symptom pairs for each drug were evaluated by our physicians on the project team. The results showed that the number of symptoms considered as real ADRs for enalapril and pravastatin was 8 and 7 out of 10, respectively.