Differential diagnosis of bone marrow failure syndromes guided by machine learning.

ABSTRACT: The choice to postpone treatment while awaiting genetic testing can result in significant delay in definitive therapies in patients with severe pancytopenia. Conversely, the misdiagnosis of inherited bone marrow failure (BMF) can expose patients to ineffectual and expensive therapies, toxic transplant conditioning regimens, and inappropriate use of an affected family member as a stem cell donor. To predict the likelihood of patients having acquired or inherited BMF, we developed a 2-step data-driven machine-learning model using 25 clinical and laboratory variables typically recorded at the initial clinical encounter. For model development, patients were labeled as having acquired or inherited BMF depending on their genomic data. Data sets were unbiasedly clustered, and an ensemble model wa

SUBMITTER: Gutierrez-Rodrigues F 

PROVIDER: S-EPMC10163315 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature