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

Formative Evaluation of Digital Health Interventions (2410) JMIR Theme Issue: COVID-19 Special Issue (2452) Machine Learning (1465) Artificial Intelligence (1371) Intensive Care Unit (ICU) (112) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) Infectious Diseases (non-STD/STI) (1575) Outbreak and Pandemic Preparedness and Management (1727) Tools, Programs and Algorithms (266) Decision Support for Health Professionals (1185) Respiratory Medicine (83)

Published on in Vol 5, No 9 (2021): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28028, first published .
Semisupervised Deep Learning Techniques for Predicting Acute Respiratory Distress Syndrome From Time-Series Clinical Data: Model Development and Validation Study

Semisupervised Deep Learning Techniques for Predicting Acute Respiratory Distress Syndrome From Time-Series Clinical Data: Model Development and Validation Study

Semisupervised Deep Learning Techniques for Predicting Acute Respiratory Distress Syndrome From Time-Series Clinical Data: Model Development and Validation Study

Authors of this article:

Carson Lam1 Author Orcid Image ;   Chak Foon Tso1 Author Orcid Image ;   Abigail Green-Saxena1 Author Orcid Image ;   Emily Pellegrini1 Author Orcid Image ;   Zohora Iqbal1 Author Orcid Image ;   Daniel Evans1 Author Orcid Image ;   Jana Hoffman1 Author Orcid Image ;   Jacob Calvert1 Author Orcid Image ;   Qingqing Mao1 Author Orcid Image ;   Ritankar Das1 Author Orcid Image
Article Authors Cited by (10) Tweetations (8) Metrics

Journals

  1. Kim Y, Chae M, Cho N, Gil H, Lee H. Machine Learning-Based Prediction Models of Acute Respiratory Failure in Patients with Acute Pesticide Poisoning. Mathematics 2022;10(24):4633 View
  2. Lam C, Thapa R, Maharjan J, Rahmani K, Tso C, Singh N, Casie Chetty S, Mao Q. Multitask Learning With Recurrent Neural Networks for Acute Respiratory Distress Syndrome Prediction Using Only Electronic Health Record Data: Model Development and Validation Study. JMIR Medical Informatics 2022;10(6):e36202 View
  3. Harrou F, Dairi A, Dorbane A, Kadri F, Sun Y. Semi-Supervised KPCA-Based Monitoring Techniques for Detecting COVID-19 Infection through Blood Tests. Diagnostics 2023;13(8):1466 View
  4. Pungitore S, Subbian V. Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review. Journal of Healthcare Informatics Research 2023;7(3):313 View
  5. Chiumello D, Coppola S, Catozzi G, Danzo F, Santus P, Radovanovic D. Lung Imaging and Artificial Intelligence in ARDS. Journal of Clinical Medicine 2024;13(2):305 View
  6. Jeong I, Kim Y, Cho N, Gil H, Lee H. A Novel Method for Medical Predictive Models in Small Data Using Out-of-Distribution Data and Transfer Learning. Mathematics 2024;12(2):237 View
  7. Ye R, Lipatov K, Diedrich D, Bhattacharyya A, Erickson B, Pickering B, Herasevich V. Automatic ARDS surveillance with chest X-ray recognition using convolutional neural networks. Journal of Critical Care 2024;82:154794 View
  8. Tran T, Tran M, Joseph A, Phan P, Grau V, Farmery A. A systematic review of machine learning models for management, prediction and classification of ARDS. Respiratory Research 2024;25(1) View
  9. Rubulotta F, Bahrami S, Marshall D, Komorowski M. Machine Learning Tools for Acute Respiratory Distress Syndrome Detection and Prediction. Critical Care Medicine 2024;52(11):1768 View
  10. Hannon D, Syed J, McNicholas B, Madden M, Laffey J. The development of a C5.0 machine learning model in a limited data set to predict early mortality in patients with ARDS undergoing an initial session of prone positioning. Intensive Care Medicine Experimental 2024;12(1) View