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The United States has an opioid epidemic, and Pennsylvania's Allegheny Countyis among the worst. This motivates a deeper exploration of what characterizesthe epidemic, such as what are risk factors for people who ultimately overdoseand die due to opioids. We show that some interesting trends and factors can beidentified from openly available autopsy data, and demonstrate the power ofbuilding an interactive data exploration tool for policy makers. However, thereis still a pressing need to incorporate further demographic factors. We showthis by using synthetic Electronic Medical Record (EMR) data to simulate thepredictive power of random forests and neural networks when given additionalloosely correlated features. In addition, we give examples of useful featureextraction that enable model enhancement without sacrificing privacy.

Characterizing Allegheny County Opioid Overdoses with an Interactive Data Explorer and Synthetic Prediction Tool