A Novel Semisupervised Algorithm for Rare Prescription Side Effect Discovery

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

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. © 2013 IEEE.

Citation

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), 537-547. https://doi.org/10.1109/JBHI.2013.2281505

• Journal Article Type: Article
• Acceptance Date: Aug 11, 2013
• Online Publication Date: Sep 11, 2013
• Publication Date: Mar 1, 2014
• Deposit Date: Sep 26, 2014
• Publicly Available Date: Sep 26, 2014
• Journal: IEEE Journal of Biomedical and Health Informatics
• Electronic ISSN: 2168-2194
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 18
• Issue: 2
• Pages: 537-547
• DOI: https://doi.org/10.1109/JBHI.2013.2281505
• Keywords: Biomedical Informatics, Data Mining
• Public URL: https://nottingham-repository.worktribe.com/output/996697
• Publisher URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6595576&sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A6754178%29
• Additional Information: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.