Semisupervised Deep Learning Techniques for Predicting Acute Respiratory Distress Syndrome From Time-Series Clinical Data: Model Development and Validation Study.

ABSTRACT:

Background

A high number of patients who are hospitalized with COVID-19 develop acute respiratory distress syndrome (ARDS).

Objective

In response to the need for clinical decision support tools to help manage the next pandemic during the early stages (ie, when limited labeled data are present), we developed machine learning algorithms that use semisupervised learning (SSL) techniques to predict ARDS development in general and COVID-19 populations based on limited labeled data.

Methods

SSL techniques were applied to 29,127 encounters with patients who were admitted to 7 US hospitals from May 1, 2019, to May 1, 2021. A recurrent neural network that used a time series of electronic health record data was applied to data that were collected when a patient's peripheral oxy

SUBMITTER: Lam C 

PROVIDER: S-EPMC8447921 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature