A Distribution-Adaptive Attention Transformer for Blood Pressure Prediction

Abstract

Accurately predicting intraoperative blood pressure to prevent intraoperative hypotension is of significant importance. However, the task of intraoperative blood pressure prediction often faces the challenge of substantial sample distribution disparities due to individual physiological differences among patients and the diversity of surgical conditions. These disparities can cause a decline in model performance when encountering inconsistently distributed samples, thus limiting its generalizability in real-world clinical settings. To address this challenge, this paper develops a distribution-adaptive attention strategy aimed at mitigating distributional differences by incorporating statistical information within the model's internal design. The strategy automatically adjusts the model's attention weights by learning statistical information from input windows, enhancing the capability to learn the inherent distribution patterns within non-stationary sequences. Consequently, it reduces performance fluctuations caused by distributional differences. The effectiveness of the proposed distribution-adaptive attention strategy is demonstrated through multiple comparative experiments in the experimental section.