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Building Trust for Postoperative Pain Estimation: Towards Explainable Machine-Learning Prediction Based on Multimodal Indicators

Postoperative pain estimation in non-verbal populations is clinically challenging due to their invalid self-reports. Existing pain estimation methods ignore the explainability and clinical utility of models, which brings a barrier to model trust. In this paper, we propose an explainable machine-learning prediction for postoperative pain. The proposed method is based on multimodal indicators with the following improvements: (1) we construct a well-benchmarked postoperative pain dataset, and multimodal indicator fusion comprising of both multiscale behavioral features from facial images and general vital signs from physiological measures is constructed; (2) Our method employs the deep stacked network (DSN) to estimate the postoperative pain, and it considers the shared feature mapping to speed up the training speed; (3) We improve the explainability and clinical utility of models through appending a SHapley Additive exPlanations (SHAP) module and a decision curve analysis (DCA) to the framework. Extensive experiments demonstrate the accuracy and efficacy of our method.