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

Machine Learning (1501) Clinical Information and Decision Making (1434) Decision Support for Health Professionals (1210) Clinical Informatics (1050)

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

Journals

  1. Zhou A, Aczon M, Laksana E, Ledbetter D, Wetzel R. Narrowing the gap: expected versus deployment performance. Journal of the American Medical Informatics Association 2023;30(9):1474 View