Background:
The immediate automatic systemic monitoring and reporting of adverse drug reaction, improving
the efficacy is the utmost need of medical informatics community. The venturing of advanced digital technologies into the
health sector has opened new avenues for rapid monitoring. In recent years, data shared through social media, mobile apps
and on other social websites has increased manifolds requiring data mining techniques.
Objective:
The objective of this report is to highlight the role of advanced technologies together with traditional methods
to proactively aid in early detection of adverse drug reactions concerned with drug safety and pharmacovigilance.
Methods:
A thorough search was conducted for papers and patents regarding pharmacivigilance. All articles with respect
to relevant subject were explored and mined from public repositories such as Pubmed, Google Scholar, Springer,
ScienceDirect (Elsevier), Web of Science, etc.
Results:
The European Union’s Innovative Medicines Initiative WEB-RADR project emphasized the development of
mobile applications and social media data for reporting adverse effects. Only relevant data has to be captured through the
data mining algorithms (DMAs) playing an important role in timely prediction of risk with high accuracy using two
popular approaches the frequentist and Bayesian approach. The pharmacovigilance at premarketing stage is useful for the
prediction of the adverse drug reactions in early developmental stage of a drug. Later postmarketing safety reports and
clinical data reports are important to be monitored through electronic health records, prescription-event monitoring,
spontaneous reporting databases, etc approaches.
Conclusion:
The advanced technologies supplemented with traditional technologies is the need of hour for evaluating
product’s risk profile and reducing risk in population esp. with comorbid conditions and on concomitant medications.
Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach (Preprint)