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

Machine Learning (2238) Artificial Intelligence (2362) Longitudinal and Cohort Studies in Public Health (440) Research Instruments, Questionnaires, and Tools (788)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/64592, first published .
Prediction of Spontaneous Breathing Trial Outcome in Critically Ill-Ventilated Patients Using Deep Learning: Development and Verification Study

Prediction of Spontaneous Breathing Trial Outcome in Critically Ill-Ventilated Patients Using Deep Learning: Development and Verification Study

Prediction of Spontaneous Breathing Trial Outcome in Critically Ill-Ventilated Patients Using Deep Learning: Development and Verification Study

Authors of this article:

Hui-Chiao Yang1 Author Orcid Image ;   Angelica Te-Hui Hao2, 3 Author Orcid Image ;   Shih-Chia Liu2, 4 Author Orcid Image ;   Yu-Cheng Chang5 Author Orcid Image ;   Yao-Te Tsai6 Author Orcid Image ;   Shao-Jen Weng4 Author Orcid Image ;   Ming-Cheng Chan7 Author Orcid Image ;   Chen-Yu Wang7 Author Orcid Image ;   Yeong-Yuh Xu8 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Arboleda A, Franco M, Naranjo F, Giraldo B. Electromyographic Diaphragm and Electrocardiographic Signal Analysis for Weaning Outcome Classification in Mechanically Ventilated Patients. Sensors 2025;25(19):6000 View
  2. Garcés M, García J, Montoya J, Triana C, Hernández Rincón E. Artificial intelligence-based models for clinical prognosis at the end of life in the ICU. International Journal of Medical Informatics 2026;206:106156 View
  3. Lee K, Ali H, Liu I, Liu W, Chien R, Chen Y, Chen Y, Pao P, Luo P, Chen K, Lee K, Chen T, Majumdar A, Kang J, Feng P, Tsai C. Extubation Decision Support in Critical Care: A Multimodal Machine Learning Framework Integrating Segmented Radiographs and Routine Clinical Data. Journal of Imaging Informatics in Medicine 2026 View