Machine Learning Models to Differentiate the Etiology of Cutaneous Reactions in Post-Stem Cell Transplant Patients
Program:
Biomedical Engineering
Project Description:
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
-
[foreach 357]
-
[if 397 not_equal=””][/if 397][395]
[/foreach 357]
Project Mentors, Sponsors, and Partners
-
David Weiner
-
Austin Burns
-
Olivia Pierog
Course Faculty
-
[foreach 429]
-
[if 433 not_equal=””][/if 433][431]
[/foreach 429]
