  Preoperative opioid use has been reported to be associated with higher
preoperative opioid demand, worse postoperative outcomes, and increased
postoperative healthcare utilization and expenditures. Understanding the risk
of preoperative opioid use helps establish patient-centered pain management. In
the field of machine learning, deep neural network (DNN) has emerged as a
powerful means for risk assessment because of its superb prediction power;
however, the blackbox algorithms may make the results less interpretable than
statistical models. Bridging the gap between the statistical and machine
learning fields, we propose a novel Interpretable Neural Network Regression
(INNER), which combines the strengths of statistical and DNN models. We use the
proposed INNER to conduct individualized risk assessment of preoperative opioid
use. Intensive simulations and an analysis of 34,186 patients expecting surgery
in the Analgesic Outcomes Study (AOS) show that the proposed INNER not only can
accurately predict the preoperative opioid use using preoperative
characteristics as DNN, but also can estimate the patient specific odds of
opioid use without pain and the odds ratio of opioid use for a unit increase in
the reported overall body pain, leading to more straightforward interpretations
of the tendency to use opioids than DNN. Our results identify the patient
characteristics that are strongly associated with opioid use and is largely
consistent with the previous findings, providing evidence that INNER is a
useful tool for individualized risk assessment of preoperative opioid use.
