Bringing biological ARDS phenotypes to the bedside with machine-learning-based classifiers

Affiliations

¹ Adult Intensive Care Unit, Townsville University Hospital, Queensland Health, Townsville, QLD, Australia. Electronic address: stephen.whebell@health.qld.gov.au.
² Department of Critical Care, King's College Hospital NHS Foundation Trust, London, UK; Institute of Global Health Innovation, Imperial College London, London, UK.

Comment

Stephen Whebell et al. Lancet Respir Med. 2022 Apr.

. 2022 Apr;10(4):319-320.

doi: 10.1016/S2213-2600(21)00492-6. Epub 2022 Jan 10.

¹ Adult Intensive Care Unit, Townsville University Hospital, Queensland Health, Townsville, QLD, Australia. Electronic address: stephen.whebell@health.qld.gov.au.
² Department of Critical Care, King's College Hospital NHS Foundation Trust, London, UK; Institute of Global Health Innovation, Imperial College London, London, UK.

No abstract available

Conflict of interest statement

We declare no competing interests.

Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis.
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