  The opioid epidemic in the United States claims over 40,000 lives per year,
and it is estimated that well over two million Americans have an opioid use
disorder. Over-prescription and misuse of prescription opioids play an
important role in the epidemic. Individuals who are prescribed opioids, and who
are diagnosed with opioid use disorder, have diverse underlying health states.
Policy interventions targeting prescription opioid use, opioid use disorder,
and overdose often fail to account for this variation. To identify latent
health states, or phenotypes, pertinent to opioid use and opioid use disorders,
we use probabilistic topic modeling with medical diagnosis histories from a
statewide population of individuals who were prescribed opioids. We demonstrate
that our learned phenotypes are predictive of future opioid use-related
outcomes. In addition, we show how the learned phenotypes can provide important
context for variability in opioid prescriptions. Understanding the
heterogeneity in individual health states and in prescription opioid use can
help identify policy interventions to address this public health crisis.
