This paper is in the following e-collection/theme issue:

Machine Learning (1401) Clinical Information and Decision Making (1340) Decision Support for Health Professionals (1147) Clinical Informatics (919)

Published on in Vol 10, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/31760, first published .
Predicting High Flow Nasal Cannula Failure in an Intensive Care Unit Using a Recurrent Neural Network With Transfer Learning and Input Data Perseveration: Retrospective Analysis

Predicting High Flow Nasal Cannula Failure in an Intensive Care Unit Using a Recurrent Neural Network With Transfer Learning and Input Data Perseveration: Retrospective Analysis

Predicting High Flow Nasal Cannula Failure in an Intensive Care Unit Using a Recurrent Neural Network With Transfer Learning and Input Data Perseveration: Retrospective Analysis

Authors of this article:

George Pappy1 Author Orcid Image ;   Melissa Aczon1 Author Orcid Image ;   Randall Wetzel1 Author Orcid Image ;   David Ledbetter1 Author Orcid Image
Article Authors Cited by (1) Tweetations (2) Metrics

George Pappy   1 * , MSc ;   Melissa Aczon   1 * , PhD ;   Randall Wetzel   1 , MBBS ;   David Ledbetter   1 * , BSc

1 The Laura P. and Leland K. Whittier Virtual PICU, Children's Hospital Los Angeles, Los Angeles, CA, United States

*these authors contributed equally

Corresponding Author:

  • David Ledbetter, BSc
  • The Laura P. and Leland K. Whittier Virtual PICU
  • Children's Hospital Los Angeles
  • 4650 Sunset Blvd
  • Los Angeles, CA, 90027
  • United States
  • Phone: 1 323 717 4515
  • Email: dledbetter@chla.usc.edu