Crossmark

Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials

Crossref DOI link: https://doi.org/10.1016/j.ebiom.2021.103697

Published Print: 2021-12

Update policy: https://doi.org/10.1016/elsevier_cm_policy

Authors

Sinha, Pratik https://orcid.org/0000-0003-3751-9079
Spicer, Alexandra

Delucchi, Kevin L

McAuley, Daniel F

Calfee, Carolyn S

Churpek, Matthew M
Funding

Funding for this research was provided by:

GlaxoSmithKline

Novartis

National Institute on Handicapped Research

National Institutes of Health

Wellcome Trust

Eli Lilly and Company

National Institute on Aging

Queen's University Belfast (USB962032)

National Heart, Lung, and Blood Institute

Innovate UK

National Institute of General Medical Sciences

Boehringer Ingelheim

Bayer Fund

National Institute on Drug Abuse

Medical Research Council

U.S. Department of Defense (W81XWH-21-1-0009)
License Information

Text and Data Mining valid from 2021-12-01

Text and Data Mining valid from 2021-12-01

Version of Record valid from 2021-11-02
More Information

This article is maintained by: Elsevier

Article Title: Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials

Journal Title: eBioMedicine

CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.ebiom.2021.103697

Content Type: article

Copyright: © 2021 The Authors. Published by Elsevier B.V.

Document is current

Any future updates will be listed below