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Machine Learning Reveals Patient Phenotypes and Stratifies Outcomes in Chronic Graft-Versus Host Disease

Year: 2018
Type: article
Abstract: Background: Classification of chronic graft-versus-host disease (cGVHD) changed dramatically with the NIH Consensus Criteria (Filipovich et al. 2005 Jagasia et al. 2015). A score of mild, moderate or severe is calculated based on multiple organ domains. We harnessed machine learning tools to classify patients according to organ scores, identify phenotypic subgroups, and determine whether revealed cGVHD subtypes shared distinct clinical outcomes.
DOI: 10.1016/j.bbmt.2017.12.635
Authors: Jocelyn S. Gandelman, Michael T. Byrne, Akshitkumar M. Mistry, Hannah Polikowsky, Kirsten E. Diggins, Heidi Chen, Stephanie J. Lee, Mukta Arora, Corey Cutler, Mary E. Flowers, Jonathan M. Irish, Madan Jagasia
Institutions: Vanderbilt University, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Fred Hutch Cancer Center, University of Minnesota, Dana-Farber Cancer Institute
Language: English
Subfield: Hematology
Field: Medicine
Open Access status: hybrid
Estimated APC paid: $3,650
OpenAlex ID: W2794407014
FWCI: 0.305
Cites:
Cited by: 2
Related to: 10