Machine Learning Classifier Models: The Future for Acute Respiratory Distress Syndrome Phenotyping?
Open Access
- 1 October 2020
- journal article
- editorial
- Published by Oxford University Press (OUP) in American Journal of Respiratory and Critical Care Medicine
- Vol. 202 (7) , 919-920
Abstract
No abstract availableThis publication has 12 references indexed in Scilit:
- Machine Learning Classifier Models Can Identify Acute Respiratory Distress Syndrome Phenotypes Using Readily Available Clinical DataAmerican Journal of Respiratory and Critical Care Medicine, 2020
- A new method for identifying the acute respiratory distress syndrome disease based on noninvasive physiological parametersPLOS ONE, 2020
- Predictive model for acute respiratory distress syndrome events in ICU patients in China using machine learning algorithms: a secondary analysis of a cohort studyJournal of Translational Medicine, 2019
- Personalised mechanical ventilation tailored to lung morphology versus low positive end-expiratory pressure for patients with acute respiratory distress syndrome in France (the LIVE study): a multicentre, single-blind, randomised controlled trialThe Lancet Respiratory Medicine, 2019
- Acute respiratory distress syndrome subphenotypes and differential response to simvastatin: secondary analysis of a randomised controlled trialThe Lancet Respiratory Medicine, 2018
- Lessons to learn from epidemiologic studies in ARDSCurrent Opinion in Critical Care, 2018
- Identification and validation of distinct biological phenotypes in patients with acute respiratory distress syndrome by cluster analysisThorax, 2017
- Acute Respiratory Distress Syndrome Subphenotypes Respond Differently to Randomized Fluid Management StrategyAmerican Journal of Respiratory and Critical Care Medicine, 2017
- Epidemiology, Patterns of Care, and Mortality for Patients With Acute Respiratory Distress Syndrome in Intensive Care Units in 50 CountriesJAMA, 2016
- Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trialsThe Lancet Respiratory Medicine, 2014