Two-Stage Spatio-Temporal Information Fusion Model for Intraoperative Hypotension Prediction
Accurately predicting intraoperative hypotension in advance has a positive impact on the selection of emergency in-terventions during surgery and the reduction of adverse risks and mortality rates in postoperative patients.Currently,the prediction of intraoperative hypotension is mainly based on various physiological indicators of patients during surgery,and existing methods fail to adequately consider the temporal and spatial information among multiple indicators.To address these issues,a two-stage spa-tio-temporal information fusion model for intraoperative hypotension prediction is proposed.It first utilizes either a fully convolution-al network or a residual network to extract local spatial information,and then employs gated recurrent units to capture temporal infor-mation for prediction.By comparing with commonly used deep neural network models for intraoperative hypotension prediction,the proposed model not only improves the prediction accuracy of hypotensive events in both original and imputed clinical data but also exhibits a certain tolerance when dealing with data imputation,thus effectively addressing the impact of noise and uncertainty.
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