  The acute respiratory distress syndrome (ARDS) is a severe form of hypoxemic
respiratory failure with in-hospital mortality of 35-46%. High mortality is
thought to be related in part to challenges in making a prompt diagnosis, which
may in turn delay implementation of evidence-based therapies. A deep neural
network (DNN) algorithm utilizing unbiased ventilator waveform data (VWD) may
help to improve screening for ARDS. We first show that a convolutional neural
network-based ARDS detection model can outperform prior work with random forest
models in AUC (0.95+/-0.019 vs. 0.88+/-0.064), accuracy (0.84+/-0.026 vs
0.80+/-0.078), and specificity (0.81+/-0.06 vs 0.71+/-0.089). Frequency
ablation studies imply that our model can learn features from low frequency
domains typically used for expert feature engineering, and high-frequency
information that may be difficult to manually featurize. Further experiments
suggest that subtle, high-frequency components of physiologic signals may
explain the superior performance of DL models over traditional ML when using
physiologic waveform data. Our observations may enable improved
interpretability of DL-based physiologic models and may improve the
understanding of how high-frequency information in physiologic data impacts the
performance our DL model.
