Response to prescribed analgesic drugs varies between individuals, and choosing the right
drug/dose often involves a lengthy, iterative process of trial and error. Furthermore, a
significant portion of patients experience adverse events such as post-operative urinary
retention (POUR) during inpatient management of acute postoperative pain. To better
forecast analgesic responses, we compared conventional machine learning methods with
modern neural network architectures to gauge their effectiveness at forecasting temporal
patterns of postoperative pain and analgesic use, as well as predicting the risk of POUR. Our
results indicate that simpler machine learning approaches might offer superior results;
however, all of these techniques may play a promising role for developing smarter post-
operative pain management strategies.