Teaching a Machine to Feel Postoperative Pain: Combining High‐Dimensional Clinical Data with Machine Learning Algorithms to Forecast Acute Postoperative Pain

• Published in: Pain medicine (Malden, Mass.) Mass.), 2015-07, Vol.16 (7), p.1386-1401
• Main Authors: Tighe, Patrick J., Harle, Christopher A., Hurley, Robert W., Aytug, Haldun, Boezaart, Andre P., Fillingim, Roger B.
• Format: Article
• Language: English
• Subjects: Algorithm; Algorithms; Decision Trees; Humans; Logistic Models; Machine Learning; Neural Networks (Computer); Pain management; Pain Prediction; Pain, Postoperative - diagnosis; Postoperative Pain; Retrospective Studies; ROC Curve
• ISSN: 1526-2375; 1526-4637
• DOI: 10.1111/pme.12713

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.