A novel semisupervised algorithm for rare prescription side effect discovery

Jenna M. Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack E. Gibson, Richard B. Hubbard

Research output: Journal PublicationArticlepeer-review

8 Citations (Scopus)

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 signaled 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 metaanalysis framework for signaling rare side effects that integrates existing methods, knowledge from the web, metric learning, and semisupervised 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 drugs investigated it generated signals for rare side effects at a more stringent signaling threshold than existing methods and shows the potential to become a fundamental part of post marketing surveillance to detect rare side effects.

Original languageEnglish
Article number6595576
Pages (from-to)537-547
Number of pages11
JournalIEEE Journal of Biomedical and Health Informatics
Volume18
Issue number2
DOIs
Publication statusPublished - Mar 2014
Externally publishedYes

Keywords

  • Adverse drug reaction (ADR)
  • HUNT
  • longitudinal healthcare
  • pharmacovigilance
  • semisupervised
  • temporal pattern discovery (TPD)

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics
  • Electrical and Electronic Engineering
  • Health Information Management

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