Intraoperative Hypotension Prediction Based on Features Automatically
Generated Within an Interpretable Deep Learning Model
Abstract
Monitoring arterial blood pressure (ABP) in anesthetized patients is
crucial for preventing hypotension, which can lead to adverse clinical
outcomes. Thus, several efforts have been made to develop an artificial
intelligence-based hypotension prediction index. Nevertheless, the use
of these indices is limited because they may not provide a convincing
interpretation of the association between predictors and hypotension.
Herein, we developed an interpretable deep learning model that forecasts
hypotension occurrences 10 min before a given 90 s ABP record. Internal
and external validations of model performance reported the area under
the receiver operating characteristic curve (AUC) as 0.9145 and 0.9035,
respectively. Furthermore, the hypotension prediction mechanism can be
physiologically interpreted by using predictors representing ABP trends
that are automatically generated in the proposed model. Ultimately, we
demonstrate high-applicability of a deep learning model that has a high
accuracy performance and provides an interpretation of the association
between ABP trends and hypotension in clinical practices.