Long short-term memory recurrent neural networks for classification of acute hypotensive episodes

Abstract

An acute hypotensive episode (AHE) is a life-threatening condition durich which a patient's mean arterial blood pressure drops below 60 mmHG for a period of 30 minutes. This thesis presents the development and evaluation of a series of Long short-term memory recurrent neural network (LSTM RNN) models which predict whether a patient will experience an AHE or not based on a time series of mean arterial blood pressure (ABP). A 2-layer, 128-hidden unit LSTM RNN trained with rmsprop and dropout regularization achieves sensitivity of 78% and specificity of 98%.