Deep Learning-Based Detection of the Acute Respiratory Distress Syndrome: What Are the Models Learning?

• URL: http://arxiv.org/abs/2109.12323v1
• Date: Sat, 25 Sep 2021 09:10:10 GMT
• Title: Deep Learning-Based Detection of the Acute Respiratory Distress Syndrome: What Are the Models Learning?
• Authors: Gregory B. Rehm, Chao Wang, Irene Cortes-Puch, Chen-Nee Chuah, Jason Adams
• Abstract summary: 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.
• Score: 5.827840113217155
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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