DoseFormer: Dynamic Graph Transformer for Postoperative Pain Prediction

Abstract

Many patients suffer from postoperative pain after surgery, which causes discomfort and influences recovery after the operation. During surgery, the anesthetists usually rely on their own experience when anesthetizing, which is not stable for avoiding postoperative pain. Hence, it is essential to predict postoperative pain and give proper doses accordingly. Recently, the relevance of various clinical parameters and nociception has been investigated in many works, and several indices have been proposed for measuring the level of nociception. However, expensive advanced equipment is required when applying advanced medical technologies, which is not accessible to most institutions. In our work, we propose a deep learning model based on a dynamic graph transformer framework named DoseFormer to predict postoperative pain in a short period after an operation utilizing dynamic patient data recorded in existing widely utilized equipment (e.g., anesthesia monitor). DoseFormer consists of two modules: (i) We design a temporal model utilizing a long short-term memory (LSTM) model with an attention mechanism to capture dynamic intraoperative data of the patient and output a hybrid semantic embedding representing the patient information. (ii) We design a graph transformer network (GTN) to infer the postoperative pain level utilizing the relations across the patient embeddings. We evaluate the DoseFormer system with the medical records of over 999 patients undergoing cardiothoracic surgery in the Fourth Affiliated Hospital of Zhejiang University School of Medicine. The experimental results show that our model achieves 92.16% accuracy for postoperative pain prediction and has a better comprehensive performance compared with baselines.