A novel machine learning model to predict respiratory failure and invasive mechanical ventilation in critically ill patients suffering from COVID-19
- DOI
- 10.1038/s41598-022-14758-x
- Published
- 2022-06-22
- Container
- Scientific Reports
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-022-14758-x,
title = {A novel machine learning model to predict respiratory failure and invasive mechanical ventilation in critically ill patients suffering from COVID-19},
author = {Itai Bendavid and Liran Statlender and Leonid Shvartser and Shmuel Teppler and Roy Azullay and Rotem Sapir and Pierre Singer},
year = {2022},
journal = {Scientific Reports},
doi = {10.1038/s41598-022-14758-x},
url = {https://doi.org/10.1038/s41598-022-14758-x}
}RIS
TY - JOUR TI - A novel machine learning model to predict respiratory failure and invasive mechanical ventilation in critically ill patients suffering from COVID-19 AU - Itai Bendavid AU - Liran Statlender AU - Leonid Shvartser AU - Shmuel Teppler AU - Roy Azullay AU - Rotem Sapir AU - Pierre Singer PY - 2022 JO - Scientific Reports DO - 10.1038/s41598-022-14758-x UR - https://doi.org/10.1038/s41598-022-14758-x ER -
APA
Bendavid, I., Statlender, L., Shvartser, L., Teppler, S., Azullay, R., Sapir, R., & Singer, P. (2022). A novel machine learning model to predict respiratory failure and invasive mechanical ventilation in critically ill patients suffering from COVID-19. Scientific Reports. https://doi.org/10.1038/s41598-022-14758-x
Source records
- crossref · retrieved 2026-09-26T18:23:37.984Z