Multimodal Pain Recognition in Postoperative Patients: A Machine Learning Approach

Abstract

AbstractObjectiveTo develop and evaluate a multimodal machine learning-based objective pain assessment algorithm on data collected from post-operative patients.MethodsThe proposed method addresses the major challenges that come with using data from such patients like the imbalanced distribution of pain classes and the scarcity of ground-truth labels. Specifically, we extracted automatic features using a convolutional autoencoder (AE) along with data augmentation techniques like weak supervision and minority oversampling to improve our models’ predictive performance. This method was used in conjunction with four different machine learning classifiers: Adaptive Boosting (AdaBoost), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) to perform binary classification on three increasing levels of pain when compared to no pain.ResultsOur models are able to recognize different pain levels with an average balanced accuracy of over 80%.ConclusionThis is the first multimodal pain recognition work done on postoperative patients and our proposed method provides valuable insights for automatic acute pain recognition in such patients.