Journal article

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

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | Published : 2014

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, knowledg..

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University of Melbourne Researchers