  There is a growing interest in using a longitudinal observational databases
to detect drug safety signal. In this paper we present a novel method, which we
used online during the OMOP Cup. We consider homogeneous ensembling, which is
based on random re-sampling (known, also, as bagging) as a main innovation
compared to the previous publications in the related field. This study is based
on a very large simulated database of the 10 million patients records, which
was created by the Observational Medical Outcomes Partnership (OMOP). Compared
to the traditional classification problem, the given data are unlabelled. The
objective of this study is to discover hidden associations between drugs and
conditions. The main idea of the approach, which we used during the OMOP Cup is
to compare the numbers of observed and expected patterns. This comparison may
be organised in several different ways, and the outcomes (base learners) may be
quite different as well. It is proposed to construct the final decision
function as an ensemble of the base learners. Our method was recognised
formally by the Organisers of the OMOP Cup as a top performing method for the
Challenge N2.
