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

Authors

Pratik Sinha, Washington University School of Medicine in St. Louis
Alexandra Spicer, University of Wisconsin- Madison
Kevin L Delucchi, University of California, San Francisco
Daniel F McAuley, Queen's University Belfast
Carolyn S Calfee, University of California, San Francisco
Matthew M Churpek, University of Wisconsin- Madison

Journal

EBioMedicine

Publication Date

12-1-2021

Volume

74

First Page

103697

Document Type

Open Access Publication

DOI

10.1016/j.ebiom.2021.103697

Rights and Permissions

Sinha P, Spicer A, Delucchi KL, McAuley DF, Calfee CS, Churpek MM. 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. EBioMedicine. 2021 Dec;74:103697. doi: 10.1016/j.ebiom.2021.103697. © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Recommended Citation

Sinha, Pratik; Spicer, Alexandra; Delucchi, Kevin L; McAuley, Daniel F; Calfee, Carolyn S; and Churpek, Matthew M, "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." EBioMedicine. 74, 103697 (2021).
https://digitalcommons.wustl.edu/open_access_pubs/11497