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Found 7 papers, 0 papers with code
Supervised Adverse Drug Reaction Signalling Framework Imitating Bradford Hill's Causality Considerations
Due to these complexities, existing methods for large-scale detection of negative side effects using observational data all tend to have issues distinguishing between association and causality.
Attributes for Causal Inference in Longitudinal Observational Databases
In this paper we investigate potential attributes that can be used in causal inference to identify side effects based on the Bradford-Hill causality criteria.
A Novel Semi-Supervised Algorithm for Rare Prescription Side Effect Discovery
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.
Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs
Conclusion: This research shows that it is possible to exploit the mechanism of causality and presents a framework for signalling adverse drug reactions effectively.
Comparing Data-mining Algorithms Developed for Longitudinal Observational Databases
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 detecting negative side effects.
Discovering Sequential Patterns in a UK General Practice Database
The wealth of computerised medical information becoming readily available presents the opportunity to examine patterns of illnesses, therapies and responses.
Investigating the Detection of Adverse Drug Events in a UK General Practice Electronic Health-Care Database
In this paper, existing methods developed for spontaneous reporting databases are implemented on both a spontaneous reporting database and a general practice electronic health-care database and compared.
