DeepAI
Log In Sign Up

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

09/25/2021
by   Gregory B Rehm, et al.
8

The acute respiratory distress syndrome (ARDS) is a severe form of hypoxemic respiratory failure with in-hospital mortality of 35-46 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.

READ FULL TEXT

page 1

page 2

page 3

page 4

05/25/2021

An Upper Limit of Decaying Rate with Respect to Frequency in Deep Neural Network

Deep neural network (DNN) usually learns the target function from low to...
06/19/2021

Parallel frequency function-deep neural network for efficient complex broadband signal approximation

A neural network is essentially a high-dimensional complex mapping model...
04/03/2022

Improving Vision Transformers by Revisiting High-frequency Components

The transformer models have shown promising effectiveness in dealing wit...
12/20/2019

Progressive transfer learning for low frequency data prediction in full waveform inversion

For the purpose of effective suppression of the cycle-skipping phenomeno...
09/16/2022

Single Image Deraining via Rain-Steaks Aware Deep Convolutional Neural Network

It is challenging to remove rain-steaks from a single rainy image becaus...
01/20/2020

Recovering Geometric Information with Learned Texture Perturbations

Regularization is used to avoid overfitting when training a neural netwo...
04/24/2018

DeepTriangle: A Deep Learning Approach to Loss Reserving

We propose a novel approach for loss reserving based on deep neural netw...