  Explainable Artificial Intelligence (XAI) has been identified as a viable
method for determining the importance of features when making predictions using
Machine Learning (ML) models. In this study, we created models that take an
individual's health information (e.g. their drug history and comorbidities) as
inputs, and predict the probability that the individual will have an Acute
Coronary Syndrome (ACS) adverse outcome. Using XAI, we quantified the
contribution that specific drugs had on these ACS predictions, thus creating an
XAI-based technique for pharmacovigilance monitoring, using ACS as an example
of the adverse outcome to detect. Individuals aged over 65 who were supplied
Musculo-skeletal system (anatomical therapeutic chemical (ATC) class M) or
Cardiovascular system (ATC class C) drugs between 1993 and 2009 were
identified, and their drug histories, comorbidities, and other key features
were extracted from linked Western Australian datasets. Multiple ML models were
trained to predict if these individuals would have an ACS related adverse
outcome (i.e., death or hospitalisation with a discharge diagnosis of ACS), and
a variety of ML and XAI techniques were used to calculate which features --
specifically which drugs -- led to these predictions. The drug dispensing
features for rofecoxib and celecoxib were found to have a greater than zero
contribution to ACS related adverse outcome predictions (on average), and it
was found that ACS related adverse outcomes can be predicted with 72% accuracy.
Furthermore, the XAI libraries LIME and SHAP were found to successfully
identify both important and unimportant features, with SHAP slightly
outperforming LIME. ML models trained on linked administrative health datasets
in tandem with XAI algorithms can successfully quantify feature importance, and
with further development, could potentially be used as pharmacovigilance
monitoring techniques.
