Abstract

Info

Title:: A deep learning method for intelligent chronic pain assessment based on spatiotemporal data analysis

Author(s):: ZHENG Rui1; WANG Cuiling2; 1. School of Medical Sciences, Shanxi Medical University, Taiyuan 030000, China 2. Department of Outpatient, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan 030009, China

Keywords:: Keywords: chronic pain spatiotemporal data deep learning physiological signal environmental information intelligent assessment

Abstract:: Objective To construct a deep learning model using spatiotemporal data (including physiological signals and environmental information) to achieve accurate prediction of chronic pain scores and provide support for intelligent pain assessment. Methods A pain score prediction model based on a residual module with an improved channel attention mechanism and BiLSTM was developed. The improved channel attention mechanism extracted key features and reduced redundant information, while BiLSTM models the temporal sequence dependencies. Improved particle swarm optimization algorithm was integrated to optimize models hyperparameters. In the experiment, the model performance was evaluated using 3 metrics: mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R2). Results The experimental results showed that the proposed model achieved an MSE of 0.117, an MAE of 0.254, and an R2 of 0.932, outperforming the comparison models such as CNN-BiLSTM and CNN-LSTM. Ablation experiment verified the critical role of the improved module in enhancing model performance, and the multimodal data experiment further demonstrated that the integration of environmental information improved the models predictive capability. Conclusion The proposed model can efficiently capture the spatiotemporal characteristics of pain scores, with high prediction accuracy and stability, and thus establishing a new method for chronic pain assessment.