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. 2015 Jul;16(7):1386-401.
doi: 10.1111/pme.12713. Epub 2015 May 29.

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

Patrick J Tighe  1 , Christopher A Harle  2 , Robert W Hurley  1 , Haldun Aytug  3 , Andre P Boezaart  1   4 , Roger B Fillingim  5
Affiliations

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

Patrick J Tighe et al. Pain Med. 2015 Jul.

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.

Keywords: Algorithm; Machine Learning; Pain Prediction; Postoperative Pain.

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Conflict of interest statement

The authors have no conflicts of interests to report.

Figures

Figure 1
Figure 1
Loading of variables into machine learning classifier pipeline. Variables were included using a staged approach for demographics, comorbidities, home medications, surgical procedure, and the circumstances of surgery.
Figure 2
Figure 2
Overview of machine learning classifier pipeline. Separate experiments were conducted for outcomes occurring on POD 1 and 3. Data replacement, imputation, and partitioning were performed using an algorithmic approach. Five machine learning classifiers were tested, along with a standard logistic regression classifier, using the entire set of variables, as well as a reduced set of variables selected via a separate feature set reduction algorithm. LASSO = Least Absolute Shrinkage and Selection Operator; SVM = Support Vector Machine; GB-D. Tree = Gradient-boosting decision tree; K-NN = k-nearest neighbor; MLP = multilayer perceptron.
Figure 3
Figure 3
ROC for pain outcomes on POD 1 and 3. The ROC for each tested classifier are shown at the training, validation, and testing stages for POD 1 (A) and POD 3 (B). For POD 1, the LASSO algorithm, using the full feature set, had the highest accuracy, with a ROC of 0.704. For POD 3, the LASSO algorithm, using the full feature set, again had the highest accuracy, with a ROC of 0.727.
Figure 3
Figure 3
ROC for pain outcomes on POD 1 and 3. The ROC for each tested classifier are shown at the training, validation, and testing stages for POD 1 (A) and POD 3 (B). For POD 1, the LASSO algorithm, using the full feature set, had the highest accuracy, with a ROC of 0.704. For POD 3, the LASSO algorithm, using the full feature set, again had the highest accuracy, with a ROC of 0.727.
Figure 4
Figure 4
Cumulative lift curves for pain outcomes on POD 1 and 3. The LASSO algorithm exhibited a cumulative lift of 1.49 given the 53% incidence of moderate to severe postoperative pain on POD 1, suggesting that at the top decile, 78% of that decile’s patients actually did suffer from severe acute postoperative pain. (A) On POD 3, the LASSO algorithm exhibited a cumulative lift of 1.61, suggesting the top decile is 1.61 times more likely to include patients with severe acute postoperative pain than would a model based on random sampling.
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