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A novel semisupervised algorithm for rare prescription side effect discovery

Reps, J.M., Garibaldi, J.M., Aickelin, U., Soria, D., Gibson, J.E., Hubbard, R.B. (2014) A novel semisupervised algorithm for rare prescription side effect discovery. IEEE Journal of Biomedical and Health Informatics, 18 (2). pp. 537-547. (doi:10.1109/JBHI.2013.2281505) (KAR id:98888)


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.

Item Type: Article
DOI/Identification number: 10.1109/JBHI.2013.2281505
Additional information: cited By 8
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Funders: University of Nottingham (
Depositing User: Daniel Soria
Date Deposited: 07 Dec 2022 15:07 UTC
Last Modified: 02 Jan 2024 10:33 UTC
Resource URI: (The current URI for this page, for reference purposes)

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