8 papers
Supervised Adverse Drug Reaction Signalling Framework Imitating Bradford Hill's Causality Considerations
Jenna Marie Reps, Jonathan M. Garibaldi, Uwe Aickelin +2
Big longitudinal observational medical data potentially hold a wealth of information and have been recognised as potential sources for gaining new drug safety knowledge. Unfortunat…
Attributes for Causal Inference in Longitudinal Observational Databases
Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin +3
The pharmaceutical industry is plagued by the problem of side effects that can occur anytime a prescribed medication is ingested. There has been a recent interest in using the vast…
Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs
Jenna M. Reps, Jonathan M. Garibaldi, Uwe Aickelin +3
Background: Children are frequently prescribed medication off-label, meaning there has not been sufficient testing of the medication to determine its safety or effectiveness. The m…
A Novel Semi-Supervised Algorithm for Rare Prescription Side Effect Discovery
Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin +3
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 u…
Comparison of algorithms that detect drug side effects using electronic healthcare databases
Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin +3
The electronic healthcare databases are starting to become more readily available and are thought to have excellent potential for generating adverse drug reaction signals. The Heal…
Comparing Data-mining Algorithms Developed for Longitudinal Observational Databases
Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin +3
Longitudinal observational databases have become a recent interest in the post marketing drug surveillance community due to their ability of presenting a new perspective for detect…