Machine Learning Classifier Models: The Future for Acute Respiratory Distress Syndrome Phenotyping?
- PMID: 32687397
- PMCID: PMC7528797
- DOI: 10.1164/rccm.202006-2388ED
Machine Learning Classifier Models: The Future for Acute Respiratory Distress Syndrome Phenotyping?
Comment on
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Machine Learning Classifier Models Can Identify Acute Respiratory Distress Syndrome Phenotypes Using Readily Available Clinical Data.Am J Respir Crit Care Med. 2020 Oct 1;202(7):996-1004. doi: 10.1164/rccm.202002-0347OC. Am J Respir Crit Care Med. 2020. PMID: 32551817 Free PMC article.
References
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- McNicholas BA, Rooney GM, Laffey JG. Lessons to learn from epidemiologic studies in ARDS. Curr Opin Crit Care. 2018;24:41–48. - PubMed
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- Bellani G, Laffey JG, Pham T, Fan E, Brochard L, Esteban A, et al. LUNG SAFE Investigators; ESICM Trials Group. Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries. JAMA. 2016;315:788–800. - PubMed
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- Calfee CS, Delucchi KL, Sinha P, Matthay MA, Hackett J, Shankar-Hari M, et al. Irish Critical Care Trials Group. Acute respiratory distress syndrome subphenotypes and differential response to simvastatin: secondary analysis of a randomised controlled trial. Lancet Respir Med. 2018;6:691–698. - PMC - PubMed
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