Semisupervised Deep Learning Techniques for Predicting Acute Respiratory Distress Syndrome From Time-Series Clinical Data: Model Development and Validation Study.
- DOI
- 10.2196/28028
- Published
- 2021 Sep 14
- Container
- JMIR formative research
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.2196/28028,
title = {Semisupervised Deep Learning Techniques for Predicting Acute Respiratory Distress Syndrome From Time-Series Clinical Data: Model Development and Validation Study.},
author = {Lam C and Tso CF and Green-Saxena A and Pellegrini E and Iqbal Z and Evans D and Hoffman J and Calvert J and Mao Q and Das R},
year = {2021},
journal = {JMIR formative research},
doi = {10.2196/28028},
url = {https://doi.org/10.2196/28028}
}RIS
TY - JOUR TI - Semisupervised Deep Learning Techniques for Predicting Acute Respiratory Distress Syndrome From Time-Series Clinical Data: Model Development and Validation Study. AU - Lam C AU - Tso CF AU - Green-Saxena A AU - Pellegrini E AU - Iqbal Z AU - Evans D AU - Hoffman J AU - Calvert J AU - Mao Q AU - Das R PY - 2021 JO - JMIR formative research DO - 10.2196/28028 UR - https://doi.org/10.2196/28028 ER -
APA
C, L., CF, T., A, G., E, P., Z, I., D, E., J, H., J, C., Q, M., & R, D. (2021). Semisupervised Deep Learning Techniques for Predicting Acute Respiratory Distress Syndrome From Time-Series Clinical Data: Model Development and Validation Study.. JMIR formative research. https://doi.org/10.2196/28028
Source records
- pubmed · retrieved 2026-09-25T05:33:12.402Z
- europe-pmc · retrieved 2026-09-25T05:33:12.410Z
- doaj · retrieved 2026-09-25T05:33:12.382Z