A Novel Semi-Supervised Algorithm for Rare Prescription Side Effect Discovery

TL;DR

This paper introduces a semi-supervised meta-analysis framework that enhances detection of rare drug side effects, outperforming existing methods and potentially improving post-marketing drug safety surveillance.

Contribution

It presents a novel computational framework combining existing techniques, web knowledge, metric learning, and semi-supervised clustering for rare side effect detection.

Findings

Successfully signalled known rare side effects like tendon rupture and renal failure.

Generated signals at more stringent thresholds than existing methods for most drugs.

Demonstrated potential to improve post-marketing surveillance for rare side effects.

Abstract

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.