Research on risk prediction of acute kidney injury based on CTSA-Net
Addressing limitations in prior research of acute kidney injury(AKI),including underutilization of clinical time series data,short predictive windows,and a lack of continuous prediction studies,we proposed a hybrid network model called CTSA-Net,in-tegrated convolutional neural networks(CNN)and a two-stage cross-attention mechanism.CTSA-Net's attention pathway,CNN path-way,and feature fusion module enhanced global representation of time series data and perception of local details,thereby improved the continuous prediction performance for AKI.At four different prediction time points at AKI onset,24,48,and 72 h before AKI onset,the model achieved respective area under the receiver operated characteristic curve(AUC)values of 0.946,0.907,0.895,and 0.879,respectively.The area under the precision-recall curve(PR-AUC)values were 0.979,0.960,0.949,and 0.939,respectively.Exper-imental results indicate that the CTSA-Net model demonstrates robust performance in predicting AKI at multiple time points,making it suitable for real-time patient monitoring and assisting clinicians in making informed clinical decisions.
Acute kidney injuryDeep learning,Electronic health recordsAttentionConvolutional neural network
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