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Open Access
Peer-reviewed
Research Article
Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients
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Roshan Karri,
Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Writing – original draft, Writing – review & editing
Affiliation Royal Melbourne Hospital, Melbourne, Victoria, Australia
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Yi-Ping Phoebe Chen,
Roles Formal analysis, Investigation, Methodology, Software, Supervision, Validation, Writing – review & editing
Affiliation Faculty of Science, Technology and Engineering, La Trobe University, Melbourne, Victoria, Australia
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Aidan J. C. Burrell,
Roles Conceptualization, Data curation, Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing
Affiliations Australian and New Zealand Intensive Care Research Centre (ANZIC-RC), School of Public Health and Preventative Medicine, Monash University, Melbourne, Victoria, Australia, Department of Intensive Care and Hyperbaric Medicine, The Alfred Hospital, Melbourne, Victoria, Australia
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Jahan C. Penny-Dimri,
Roles Formal analysis, Investigation, Methodology, Software, Supervision, Writing – review & editing
Affiliation Royal Melbourne Hospital, Melbourne, Victoria, Australia
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Tessa Broadley,
Roles Conceptualization, Data curation, Project administration, Resources, Supervision, Writing – review & editing
Affiliation Australian and New Zealand Intensive Care Research Centre (ANZIC-RC), School of Public Health and Preventative Medicine, Monash University, Melbourne, Victoria, Australia
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Tony Trapani,
Roles Data curation, Project administration, Resources, Writing – review & editing
Affiliation Australian and New Zealand Intensive Care Research Centre (ANZIC-RC), School of Public Health and Preventative Medicine, Monash University, Melbourne, Victoria, Australia
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Adam M. Deane,
Roles Methodology, Project administration, Resources, Supervision, Writing – review & editing
Affiliations Intensive Care Unit, Royal Melbourne Hospital, Melbourne, Victoria, Australia, Department of Critical Care, Melbourne Medical School, Melbourne, Victoria, Australia
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Andrew A. Udy,
Roles Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing
Affiliations Australian and New Zealand Intensive Care Research Centre (ANZIC-RC), School of Public Health and Preventative Medicine, Monash University, Melbourne, Victoria, Australia, Department of Intensive Care and Hyperbaric Medicine, The Alfred Hospital, Melbourne, Victoria, Australia
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Mark P. Plummer ,
Roles Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing
* E-mail: Mark.plummer@mh.org.au
Affiliations Intensive Care Unit, Royal Melbourne Hospital, Melbourne, Victoria, Australia, Department of Critical Care, Melbourne Medical School, Melbourne, Victoria, Australia
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for the SPRINT-SARI Australia Investigators
¶Membership of the SPRINT-SARI Australia Investigators is listed in the Acknowledgments.
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Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients
- Roshan Karri,
- Yi-Ping Phoebe Chen,
- Aidan J. C. Burrell,
- Jahan C. Penny-Dimri,
- Tessa Broadley,
- Tony Trapani,
- Adam M. Deane,
- Andrew A. Udy,
- Mark P. Plummer,
- for the SPRINT-SARI Australia Investigators
- Published: October 26, 2022
- https://doi.org/10.1371/journal.pone.0276509