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

Background: Chronic graft-versus-host disease (cGVHD) contributes to significant morbidity and mortality post hematopoietic stem cell transplant. Establishing a reliable classification system for this biologically and clinically heterogenous disease, remains challenging. Current scoring systems (e.g., NIH consensus criteria) calculate a score of mild, moderate, or severe disease from multiple organ domains. However, important information about the biology of disease and subtypes of patients may be lost when using the aggregate NIH overall severity classification that combines multiple dimensions of organ data. Machine learning may thus reveal subgroups in multi-dimensional data.