Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients.
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| Title: | Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients. |
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| Authors: | Karri R; Royal Melbourne Hospital, Melbourne, Victoria, Australia., Chen YP; Faculty of Science, Technology and Engineering, La Trobe University, Melbourne, Victoria, Australia., Burrell AJC; 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., Penny-Dimri JC; Royal Melbourne Hospital, Melbourne, Victoria, Australia., Broadley T; Australian and New Zealand Intensive Care Research Centre (ANZIC-RC), School of Public Health and Preventative Medicine, Monash University, Melbourne, Victoria, Australia., Trapani T; Australian and New Zealand Intensive Care Research Centre (ANZIC-RC), School of Public Health and Preventative Medicine, Monash University, Melbourne, Victoria, Australia., Deane AM; Intensive Care Unit, Royal Melbourne Hospital, Melbourne, Victoria, Australia.; Department of Critical Care, Melbourne Medical School, Melbourne, Victoria, Australia., Udy AA; 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., Plummer MP; Intensive Care Unit, Royal Melbourne Hospital, Melbourne, Victoria, Australia.; Department of Critical Care, Melbourne Medical School, Melbourne, Victoria, Australia. |
| Corporate Authors: | SPRINT-SARI Australia Investigators |
| Source: | PloS one [PLoS One] 2022 Oct 26; Vol. 17 (10), pp. e0276509. Date of Electronic Publication: 2022 Oct 26 (Print Publication: 2022). |
| Publication Type: | Journal Article; Research Support, Non-U.S. Gov't |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0276509 |
