A Novel Semi-Supervised Algorithm for Rare Prescription Side Effect Discovery
This addresses the issue of delayed detection of rare side effects in drug surveillance, which risks patient morbidity or mortality, though it appears incremental as it builds upon and integrates existing methods.
The paper tackled the problem of detecting rare prescription drug side effects by proposing a computational meta-analysis framework that integrates existing methods, web knowledge, metric learning, and semi-supervised clustering, resulting in the ability to signal known rare side effects like tendon rupture and renal failure at a more stringent threshold than existing methods.
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.