DoseGuide: A Graph-based Dynamic Time-aware Prediction System for Postoperative Pain

Postoperative pain cause discomfort to the patient, and even postoperative complications in severe cases, which suggests there is a severe need for predicting the postoperative pain. A number of studies have investigated the correlation between different physiological parameters and nociception, and developed indicators for evaluating the degree of intraoperative nociception. However, these technologies require additional monitoring equipment, which increases the difficulty of deployment and popularization of postoperative pain prediction. In this paper, We propose DoseGuide, a graph-based dynamic time-aware prediction system based on the patient data collected from existing standard infrastructure. DoseGuide takes as input the static physical data and the dynamic intraoperative data of the patient, and output the prediction of postoperative pain level for the certain patient, in which the two types of features are fused via a hybrid feature encoder. Additionally, a graph attention mechanism is introduced to utilize the similarity relationships between patients, which promoted the accuracy of prediction further. We evaluate the system with the medical records of 999 patients undergoing cardiothoracic surgery in the Fourth Affiliated Hospital of Zhejiang University School of Medicine. The Experimental results show that our model achieves 78% accuracy for postoperative pain, and has the best comprehensive performance in comparison with baselines.