Predicting Failure of Noninvasive Respiratory Support Using Deep Recurrent Learning.

ABSTRACT:

Background

Noninvasive respiratory support (NRS) is increasingly used to support patients with acute respiratory failure. However, noninvasive support failure may worsen outcomes compared to primary support with invasive mechanical ventilation. Therefore, there is a need to identify patients where NRS is failing so that treatment can be reassessed and adjusted. The objective of this study was to develop and evaluate 3 recurrent neural network (RNN) models to predict NRS failure.

Methods

This was a cross-sectional observational study to evaluate the ability of deep RNN models (long short-term memory [LSTM], gated recurrent unit [GRU]), and GRU with trainable decay) to predict failure of NRS. Data were extracted from electronic health records from all adult (≥ 18 y) patient re

SUBMITTER: Essay PT 

PROVIDER: S-EPMC10173118 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature