计算机与数字工程2024，Vol.52Issue(1)：43-50,98,9.DOI:10.3969/j.issn.1672-9722.2024.01.007
一种通道自适应与局部增强的Transformer术中血压预测方法
A Transformer Intraoperative Blood Pressure Prediction Method Based on Channel Adaptation and Local Enhancement
摘要
Abstract
Accurately predicting the intraoperative blood pressure status of patients to prevent intraoperative hypotension has a positive effect on improving surgical safety and reducing postoperative complications.Previous hypotension prediction methods were mainly regarded as binary classification tasks,ignoring the process of patient blood pressure changes,thus limiting the formu-lation of intervention strategies.Therefore,predicting the change trend of blood pressure in advance has more important clinical re-search and application value.In this study,it focuses on the real-time prediction of future blood pressure values at 5min,10min and 15min using monitored intraoperative physiological sequences.A Channel-Adaptive and Locally-Enhanced Transformer model is proposed,which captures the local similarity of blood pressure sequences using convolutional attention mechanisms and incorpo-rates a Channel-Adaptive module to model the underlying interactions in physiological sequences.Experimental results show that the proposed model achieves significant improvements in prediction accuracy at 5min,10min and 15min,with respective increases of 4.88%,8.2%and 8.42%compared to the baseline model.The Mean Absolute Error(MAE)for predicted mean arterial pressure is 2.997,3.393 and 3.743,respectively,outperforming other comparative models significantly.The findings provide a new solution for intraoperative blood pressure prediction.关键词
术中血压预测/Transformer/生理序列/注意力机制/通道自适应Key words
intraoperative blood pressure prediction/Transformer/physiological sequence/attention mechanism/channel adaptation分类
信息技术与安全科学引用本文复制引用
王尘,蔡晶晶,郝学超,张伟义,舒红平,王亚强,陈果..一种通道自适应与局部增强的Transformer术中血压预测方法[J].计算机与数字工程,2024,52(1):43-50,98,9.基金项目
四川大学华西医院"学科卓越发展1·3·5工程"交叉学科创新项目(编号:2023H022) （编号:2023H022）
四川大学华西医院1·3·5项目(编号:ZYJC21008) （编号:ZYJC21008）
国家重点研发计划项目(编号:2018YFC2001800)资助. （编号:2018YFC2001800）
