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

Carson Lam   1 , MD ;   Chak Foon Tso   1 , PhD ;   Abigail Green-Saxena   1 , PhD ;   Emily Pellegrini   1 , MEng ;   Zohora Iqbal   1 , PhD ;   Daniel Evans   1 , MS ;   Jana Hoffman   1 , PhD ;   Jacob Calvert   1 , MSc ;   Qingqing Mao   1 , PhD ;   Ritankar Das   1 , MSc

1 Dascena, Inc, Houston, TX, United States

Corresponding Author:

  • Carson Lam, MD
  • Dascena, Inc
  • Suite B, Private Mailbox 65148
  • 12333 Sowden Rd
  • Houston, TX, 77080
  • United States
  • Phone: 1 7149326188
  • Email: clam@dascena.com