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

Machine Learning (1738) Clinical Information and Decision Making (1656) Decision Support for Health Professionals (1338) Clinical Informatics (1162)

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 (2) 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
  2. Chiaruttini M, Lorenzoni G, Daverio M, Marchetto L, Izzo F, Chidini G, Picconi E, Nettuno C, Zanonato E, Sagredini R, Rossetti E, Mondardini M, Cecchetti C, Vitale P, Alaimo N, Colosimo D, Sacco F, Genoni G, Perrotta D, Micalizzi C, Moggia S, Chisari G, Rulli I, Wolfler A, Amigoni A, Gregori D. Non-Invasive Ventilation Failure in Pediatric ICU: A Machine Learning Driven Prediction. Diagnostics 2024;14(24):2857 View