  Side effects of prescribed medications are a common occurrence. Electronic
healthcare databases present the opportunity to identify new side effects
efficiently but currently the methods are limited due to confounding (i.e. when
an association between two variables is identified due to them both being
associated to a third variable).
  In this paper we propose a proof of concept method that learns common
associations and uses this knowledge to automatically refine side effect
signals (i.e. exposure-outcome associations) by removing instances of the
exposure-outcome associations that are caused by confounding. This leaves the
signal instances that are most likely to correspond to true side effect
occurrences. We then calculate a novel measure termed the confounding-adjusted
risk value, a more accurate absolute risk value of a patient experiencing the
outcome within 60 days of the exposure.
  Tentative results suggest that the method works. For the four signals (i.e.
exposure-outcome associations) investigated we are able to correctly filter the
majority of exposure-outcome instances that were unlikely to correspond to true
side effects. The method is likely to improve when tuning the association rule
mining parameters for specific health outcomes.
  This paper shows that it may be possible to filter signals at a patient level
based on association rules learned from considering patients' medical
histories. However, additional work is required to develop a way to automate
the tuning of the method's parameters.
