Detecting Ineffective Efforts during Expiration for Neonates with Attention RNNs

Abstract Patient-ventilator asynchronies occur during mechanical ventilation when there is a mismatch between the patient’s needs and the ventilator’s settings. Ineffective Efforts during Expiration (IEE) is such an asynchrony, which if undetected can cause patient stress and prolong the ventilation duration. Current approaches to detect IEEs only target adult patients and are not directly applicable to newborns. This work explores the use of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) models to detect IEEs in neonates. An attention mechanism is employed to additionally offer a visual explanation for the network’s classification. All of the tested attention RNN architectures yield an accuracy of over 90%, with LSTMs performing slightly better than simple RNNs. A user study is conducted to evaluate the usability of the employed explainability method. The results show that the used visualizations are intuitive to understand, however the network’s attention itself can be misleading in certain cases.