title: Teaching a Machine to Feel Postoperative Pain: Combining High-Dimensional Clinical Data with Machine Learning Algorithms to Forecast Acute Postoperative Pain.
abstract: BACKGROUND
  Given their ability to process highly dimensional datasets with hundreds of variables, machine learning algorithms may offer one solution to the vexing challenge of predicting postoperative pain.
  
  
  METHODS
  Here, we report on the application of machine learning algorithms to predict postoperative pain outcomes in a retrospective cohort of 8,071 surgical patients using 796 clinical variables. Five algorithms were compared in terms of their ability to forecast moderate to severe postoperative pain: Least Absolute Shrinkage and Selection Operator (LASSO), gradient-boosted decision tree, support vector machine, neural network, and k-nearest neighbor (k-NN), with logistic regression included for baseline comparison.
  
  
  RESULTS
  In forecasting moderate to severe postoperative pain for postoperative day (POD) 1, the LASSO algorithm, using all 796 variables, had the highest accuracy with an area under the receiver-operating curve (ROC) of 0.704. Next, the gradient-boosted decision tree had an ROC of 0.665 and the k-NN algorithm had an ROC of 0.643. For POD 3, the LASSO algorithm, using all variables, again had the highest accuracy, with an ROC of 0.727. Logistic regression had a lower ROC of 0.5 for predicting pain outcomes on POD 1 and 3.
  
  
  CONCLUSIONS
  Machine learning algorithms, when combined with complex and heterogeneous data from electronic medical record systems, can forecast acute postoperative pain outcomes with accuracies similar to methods that rely only on variables specifically collected for pain outcome prediction.
authors.affiliation: Department of Anesthesiology, University of Florida College of Medicine, Gainesville, Florida, USA.
authors.forename: Patrick J
authors.initials: PJ
authors.lastname: Tighe
authors.affiliation: Department of Health Services Research, Management and Policy, College of Public Health and Health Professions, Gainesville, Florida, USA.
authors.forename: Christopher A
authors.initials: CA
authors.lastname: Harle
authors.affiliation: Department of Anesthesiology, University of Florida College of Medicine, Gainesville, Florida, USA.
authors.forename: Robert W
authors.initials: RW
authors.lastname: Hurley
authors.affiliation: Department of Information Systems and Operations Management, Warrington College of Business Administration, Gainesville, Florida, USA.
authors.forename: Haldun
authors.initials: H
authors.lastname: Aytug
authors.affiliation: Department of Anesthesiology, University of Florida College of Medicine, Gainesville, Florida, USA.|Department of Orthopaedic Surgery and Rehabilitation, University of Florida College of Medicine, Gainesville, Florida, USA.
authors.forename: Andre P
authors.initials: AP
authors.lastname: Boezaart
authors.affiliation: Department of Community Dentistry and Behavioral Science, University of Florida College of Dentistry, Gainesville, Florida, USA.
authors.forename: Roger B
authors.initials: RB
authors.lastname: Fillingim
mesh_terms: D000465:Algorithms; D003663:Decision Trees; D006801:Humans; D016015:Logistic Models; D000069550:Machine Learning; D016571:Neural Networks, Computer; D010149:Postoperative Pain / Q000175:diagnosis*; D012372:ROC Curve; D012189:Retrospective Studies
keywords: Algorithm; Machine Learning; Pain Prediction; Postoperative Pain