  Drugs are frequently prescribed to patients with the aim of improving each
patient's medical state, but an unfortunate consequence of most prescription
drugs is the occurrence of undesirable side effects. Side effects that occur in
more than one in a thousand patients are likely to be signalled efficiently by
current drug surveillance methods, however, these same methods may take decades
before generating signals for rarer side effects, risking medical morbidity or
mortality in patients prescribed the drug while the rare side effect is
undiscovered. In this paper we propose a novel computational meta-analysis
framework for signalling rare side effects that integrates existing methods,
knowledge from the web, metric learning and semi-supervised clustering. The
novel framework was able to signal many known rare and serious side effects for
the selection of drugs investigated, such as tendon rupture when prescribed
Ciprofloxacin or Levofloxacin, renal failure with Naproxen and depression
associated with Rimonabant. Furthermore, for the majority of the drug
investigated it generated signals for rare side effects at a more stringent
signalling threshold than existing methods and shows the potential to become a
fundamental part of post marketing surveillance to detect rare side effects.
