  The United States has an opioid epidemic, and Pennsylvania's Allegheny County
is among the worst. This motivates a deeper exploration of what characterizes
the epidemic, such as what are risk factors for people who ultimately overdose
and die due to opioids. We show that some interesting trends and factors can be
identified from openly available autopsy data, and demonstrate the power of
building an interactive data exploration tool for policy makers. However, there
is still a pressing need to incorporate further demographic factors. We show
this by using synthetic Electronic Medical Record (EMR) data to simulate the
predictive power of random forests and neural networks when given additional
loosely correlated features. In addition, we give examples of useful feature
extraction that enable model enhancement without sacrificing privacy.
