Genomic-Based Machine Learning Towards Prediction of the Etiology of Bone Marrow Failure Syndromes

Genetic testing has been increasingly used to assist with differential diagnosis of acquired vs inherited bone marrow failure syndromes (IBMFS), a group of rare and heterogeneous diseases. However, the assay is still costly and not routinely available for many hematologists. To improve decision-making for genetic testing, we developed a genomic-based machine-learning model based on a two-step data-driven clustering and classification process to predict the likelihood of BMF patients having either an acquired or inherited disease based on 27 clinical and laboratory variables recorded at initial clinical encounter.