  The increased adoption of Electronic Health Records(EHRs) has brought changes
to the way the patient care is carried out. The rich heterogeneous and temporal
data space stored in EHRs can be leveraged by machine learning models to
capture the underlying information and make clinically relevant predictions.
This can be exploited to support public health activities such as
pharmacovigilance and specifically mitigate the public health issue of Adverse
Drug Events(ADEs). The aim of this article is, therefore, to investigate the
various ways of handling temporal data for the purpose of detecting ADEs. Based
on a review of the existing literature, 11 articles from the last 10 years were
chosen to be studied. According to the literature retrieved the main methods
were found to fall into 5 different approaches: based on temporal abstraction,
graph-based, learning weights and data tables containing time series of
different length. To that end, EHRs are a valuable source that has led current
research to the automatic detection of ADEs. Yet there still exists a great
deal of challenges that concerns the exploitation of the heterogeneous, data
types with temporal information included in EHRs for predicting ADEs.
