Identifying Candidate Risk Factors for Prescription Drug Side Effects
  using Causal Contrast Set Mining
  Big longitudinal observational databases present the opportunity to extract
new knowledge in a cost effective manner. Unfortunately, the ability of these
databases to be used for causal inference is limited due to the passive way in
which the data are collected resulting in various forms of bias. In this paper
we investigate a method that can overcome these limitations and determine
causal contrast set rules efficiently from big data. In particular, we present
a new methodology for the purpose of identifying risk factors that increase a
patients likelihood of experiencing the known rare side effect of renal failure
after ingesting aminosalicylates. The results show that the methodology was
able to identify previously researched risk factors such as being prescribed
diuretics and highlighted that patients with a higher than average risk of
renal failure may be even more susceptible to experiencing it as a side effect
after ingesting aminosalicylates.
