Allogeneic hematopoietic stem cell transplantation (HSCT) is a life-saving treatment for patients with hematologic diseases and is often complicated by post-transplant cutaneous eruptions. The etiologies of these rashes include drug reactions, viral exanthema, and graft-versus-host disease (GVHD), which require timely and unique treatment regimens to lessen associated morbidity and mortality. Differentiation of post-HSCT rashes is challenging within current diagnostic paradigms. Therefore, there is a need for robust computational models that can integrate clinical and lab data to appropriately identify post-HSCT rash etiology. In this investigation, we employ supervised and unsupervised machine learning models ad a single-institution dataset to help distinguish GVHD and non-GVHD rashes.

Team Members

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Project Mentors, Sponsors, and Partners

• Sima Rozati

• David Weiner

• Austin Burns

• Olivia Pierog

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