IEEE Trans Neural Netw Learn Syst. 2024 Oct;35(10):13887-13901. doi: 10.1109/TNNLS.2023.3273187. Epub 2024 Oct 7.
The monitoring of arterial blood pressure (ABP) in anesthetized patients is crucial for preventing hypotension, which can lead to adverse clinical outcomes. Several efforts have been devoted to develop artificial intelligence-based hypotension prediction indices. However, the use of such indices is limited because they may not provide a compelling interpretation of the association between the predictors and hypotension. Herein, an interpretable deep learning model is developed that forecasts hypotension occurrence 10 min before a given 90-s ABP record. Internal and external validations of the model performance show the area under the receiver operating characteristic curves of 0.9145 and 0.9035, respectively. Furthermore, the hypotension prediction mechanism can be physiologically interpreted using the predictors automatically generated from the proposed model for representing ABP trends. Finally, the applicability of a deep learning model with high accuracy is demonstrated, thus providing an interpretation of the association between ABP trends and hypotension in clinical practice.
监测麻醉患者的动脉血压（ABP）对于预防低血压至关重要，因为低血压可能导致不良的临床结局。已经有多项努力致力于开发基于人工智能的低血压预测指标。然而，由于这些指标可能无法对预测因子与低血压之间的关联提供令人信服的解释，因此其使用受到限制。在此，开发了一种可解释的深度学习模型，该模型可以在给定的 90 秒 ABP 记录之前 10 分钟预测低血压的发生。模型性能的内部和外部验证分别显示出接收者操作特征曲线下的面积为 0.9145 和 0.9035。此外，可以使用从所提出的模型自动生成的预测因子来解释低血压预测机制，这些预测因子用于表示 ABP 趋势。最后，展示了具有高精度的深度学习模型的适用性，从而为临床实践中 ABP 趋势与低血压之间的关联提供了一种解释。
