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Determining whether hypotensive patients in intensive care units (ICUs)should receive fluid bolus therapy (FBT) has been an extremely challenging taskfor intensive care physicians as the corresponding increase in blood pressurehas been hard to predict. Our study utilized regression models andattention-based recurrent neural network (RNN) algorithms and a multi-clinicalinformation system large-scale database to build models that can predict thesuccessful response to FBT among hypotensive patients in ICUs. We investigatedboth time-aggregated modeling using logistic regression algorithms withregularization and time-series modeling using the long short term memorynetwork (LSTM) and the gated recurrent units network (GRU) with the attentionmechanism for clinical interpretability. Among all modeling strategies, thestacked LSTM with the attention mechanism yielded the most predictable modelwith the highest accuracy of 0.852 and area under the curve (AUC) value of0.925. The study results may help identify hypotensive patients in ICUs whowill have sufficient blood pressure recovery after FBT.

Predicting Blood Pressure Response to Fluid Bolus Therapy Using Attention-Based Neural Networks for Clinical Interpretability