Machine learning reveals chronic graft-<i>versus</i>-host disease phenotypes and stratifies survival after stem cell transplant for hematologic malignancies.

ABSTRACT: The application of machine learning in medicine has been productive in multiple fields, but has not previously been applied to analyze the complexity of organ involvement by chronic graft-versus-host disease. Chronic graft-versus-host disease is classified by an overall composite score as mild, moderate or severe, which may overlook clinically relevant patterns in organ involvement. Here we applied a novel computational approach to chronic graft-versus-host disease with the goal of identifying phenotypic groups based on the subcomponents of the National Institutes of Health Consensus Criteria. Computational analysis revealed seven distinct groups of patients with contrasting clinical risks. The high-risk group had an inferior overall survival compared to the low-risk g

SUBMITTER: Gandelman JS 

PROVIDER: S-EPMC6312024 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature