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

Clinical engineering (7) Machine Learning (1867) Pulmonology, COPD (179) New Methods (1073) Development of Novel Medical Devices and Innovations for Existing Devices (92)

Published on in Vol 9 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48497, first published .
A Deep Learning Framework for Predicting Patient Decannulation on Extracorporeal Membrane Oxygenation Devices: Development and Model Analysis Study

A Deep Learning Framework for Predicting Patient Decannulation on Extracorporeal Membrane Oxygenation Devices: Development and Model Analysis Study

A Deep Learning Framework for Predicting Patient Decannulation on Extracorporeal Membrane Oxygenation Devices: Development and Model Analysis Study

Authors of this article:

Joshua Fuller1 Author Orcid Image ;   Alexey Abramov2 Author Orcid Image ;   Dana Mullin3 Author Orcid Image ;   James Beck3 Author Orcid Image ;   Philippe Lemaitre2 Author Orcid Image ;   Elham Azizi4, 5, 6, 7 Author Orcid Image
Article Authors Cited by (4) Tweetations (3) Metrics

Journals

  1. Karthika M, Sreedharan J, Shevade M, Mathew C, Ray S. Artificial intelligence in respiratory care. Frontiers in Digital Health 2024;6 View
  2. Sarma D, Rali A, Jentzer J. Key Concepts in Machine Learning and Clinical Applications in the Cardiac Intensive Care Unit. Current Cardiology Reports 2025;27(1) View
  3. Oza P, Gupta A, Shamarao S, Marreddy S, Kannamani B, Rakam K, Sathyavadhi A, Singamsetty A, Lakkireddygari S. Translational Medicine and Its Role in ECMO: A Narrative Review. Indian Journal of ECMO 2025;3(1):15 View
  4. Chen S, Liu L, Hu D, Zhu Y, Wu C, Fu H, Wu J, Zhang K, Chang M, Hao T, Wang W, Liu S. Effect of artificial intelligence in extracorporeal membrane oxygenation: a systematic review and meta-analysis. Intensive and Critical Care Nursing 2026;92:104165 View