  We describe two recently proposed machine learning approaches for discovering
emerging trends in fatal accidental drug overdoses. The Gaussian Process Subset
Scan enables early detection of emerging patterns in spatio-temporal data,
accounting for both the non-iid nature of the data and the fact that detecting
subtle patterns requires integration of information across multiple spatial
areas and multiple time steps. We apply this approach to 17 years of
county-aggregated data for monthly opioid overdose deaths in the New York City
metropolitan area, showing clear advantages in the utility of discovered
patterns as compared to typical anomaly detection approaches.
  To detect and characterize emerging overdose patterns that differentially
affect a subpopulation of the data, including geographic, demographic, and
behavioral patterns (e.g., which combinations of drugs are involved), we apply
the Multidimensional Tensor Scan to 8 years of case-level overdose data from
Allegheny County, PA. We discover previously unidentified overdose patterns
which reveal unusual demographic clusters, show impacts of drug legislation,
and demonstrate potential for early detection and targeted intervention. These
approaches to early detection of overdose patterns can inform prevention and
response efforts, as well as understanding the effects of policy changes.
