Use of Machine-Learning Classifiers to Predict Requests for Preoperative Acute Pain Service Consultation

OBJECTIVE: The purpose of this project was to determine whether machine-learning classifiers could predict which patients would require a preoperative acute pain service (APS) consultation. DESIGN: Retrospective cohort. SETTING: University teaching hospital. SUBJECTS: The records of 9,860 surgical patients posted between January 1 and June 30, 2010 were reviewed. OUTCOME MEASURES: Request for APS consultation. A cohort of machine-learning classifiers was compared according to its ability or inability to classify surgical cases as requiring a request for a preoperative APS consultation. Classifiers were then optimized utilizing ensemble techniques. Computational efficiency was measured with the central processing unit processing times required for model training. Classifiers were tested using the full feature set, as well as the reduced feature set that was optimized using a merit-based dimensional reduction strategy. RESULTS: Machine-learning classifiers correctly predicted preoperative requests for APS consultations in 92.3% (95% confidence intervals CI, 91.8-92.8) of all surgical cases. Bayesian methods yielded the highest area under the receiver operating curve (0.87, 95% CI 0.84-0.89) and lowest training times (0.0018 seconds, 95% CI, 0.0017-0.0019 for the NaiveBayesUpdateable algorithm). An ensemble of high-performing machine-learning classifiers did not yield a higher area under the receiver operating curve than its component classifiers. Dimensional reduction decreased the computational requirements for multiple classifiers, but did not adversely affect classification performance. CONCLUSIONS: Using historical data, machine-learning classifiers can predict which surgical cases should prompt a preoperative request for an APS consultation. Dimensional reduction improved computational efficiency and preserved predictive performance.