title: Deep neural network architectures for forecasting analgesic response.
abstract: 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.
authors.forename: Paul
authors.initials: P
authors.lastname: Nickerson
authors.forename: Patrick
authors.initials: P
authors.lastname: Tighe
authors.forename: Benjamin
authors.initials: B
authors.lastname: Shickel
authors.forename: Parisa
authors.initials: P
authors.lastname: Rashidi
mesh_terms: D000284:Administration, Oral; D000698:Analgesia; D000700:Analgesics / Q000008:administration & dosage / Q000494:pharmacology / Q000627:therapeutic use; D006801:Humans; D000069550:Machine Learning; D016571:Neural Networks, Computer; D010098:Oxycodone / Q000008:administration & dosage / Q000494:pharmacology / Q000627:therapeutic use; D010147:Pain Measurement / Q000379:methods*; D010149:Postoperative Pain / Q000188:drug therapy / Q000209:etiology; D011183:Postoperative Complications / Q000209:etiology; D060388:Support Vector Machine; D016055:Urinary Retention / Q000209:etiology
chemical_list: D000700:Analgesics; D010098:Oxycodone