Changing paradigms in detecting rare adverse drug reactions: from disproportionality analysis, old and new, to machine learning
Daniel Arku, Consuela Cheriece Yousef, Ivo Abraham
Abstract
PLAIN LANGUAGE SUMMARYYour physician, pharmacist, nurse, or even you can voluntarily report suspected adverse events associated with drugs. The FDA Adverse Reporting System (FAERS) and the WHO Vigibase are large databases that store individual reports of adverse drug reactions (ADRs). While some ADRs are very common, others are seen rarely. Detecting rare and very rare ADRs is extremely difficult but very important for the safe use of drugs. Databases such as FAERS and WHO Vigibase contain a large amount of data and are commonly used for analysis applying a statistical method called disproportionately analysis. This type of analysis determines whether there is a higher-than-expected number of adverse reactions for a particular drug. In the future, machine learning will complement this process by applying algorithms to the data, constructing and refining rules of inference, and building predictive models of ADRs. This paradigm change in testing for ADRs is expected to provide a better understanding of the factors impacting drug safety.