  Big longitudinal observational medical data potentially hold a wealth of
information and have been recognised as potential sources for gaining new drug
safety knowledge. Unfortunately there are many complexities and underlying
issues when analysing longitudinal observational data. Due to these
complexities, existing methods for large-scale detection of negative side
effects using observational data all tend to have issues distinguishing between
association and causality. New methods that can better discriminate causal and
non-causal relationships need to be developed to fully utilise the data. In
this paper we propose using a set of causality considerations developed by the
epidemiologist Bradford Hill as a basis for engineering features that enable
the application of supervised learning for the problem of detecting negative
side effects. The Bradford Hill considerations look at various perspectives of
a drug and outcome relationship to determine whether it shows causal traits. We
taught a classifier to find patterns within these perspectives and it learned
to discriminate between association and causality. The novelty of this research
is the combination of supervised learning and Bradford Hill's causality
considerations to automate the Bradford Hill's causality assessment. We
evaluated the framework on a drug safety gold standard know as the
observational medical outcomes partnership's nonspecified association reference
set. The methodology obtained excellent discriminate ability with area under
the curves ranging between 0.792-0.940 (existing method optimal: 0.73) and a
mean average precision of 0.640 (existing method optimal: 0.141). The proposed
features can be calculated efficiently and be readily updated, making the
framework suitable for big observational data.
