Deep Learning Approaches for Understanding Adverse Drug Reaction: Short Literature Review

An adverse drug reaction (ADR) is a harmful disarray that occurs as a result of taking a drug at doses commonly used in humans for the prevention, diagnosis, treatment of disease, or to change physiological function. The detection of ADRs is critical to our understanding of risk-benefit profiles. Reporting ADRs has become an essential part of the monitoring and evaluation activities carried out in hospitals. Spontaneous reporting of ADRs has long been the standard method of reporting. However, this approach is known to have high underreporting rates, a problem that limits pharmacovigilance efforts. Deep Learning (DL) techniques are now widely used for drawing useful information from unstructured textual data. The review begins with an explanation of ADRs. It also underscores how traditional methods are inadequate to deal with the large volume of unstructured information. The following parts describe major methods of DL applied in ADR detection.