In this study, we propose a conceptual framework of decision support tools, built upon machine learning and multi-objective optimization, aimed at offering a deeper understanding of the complex trade-offs involved in hematopoietic stem cell transplan...
Orphan diseases, exemplified by T-cell prolymphocytic leukemia, present inherent challenges due to limited data availability and complexities in effective care. This study delves into harnessing the potential of machine learning to enhance care strat...
Transplantation-associated thrombotic microangiopathy (TA-TMA) is a complication of allogeneic hematopoietic cell transplantation (HCT) that often occurs following the development of acute graft-versus-host disease (aGVHD). In this study, we aimed to...
BACKGROUND:  The acute graft-versus-host disease (aGvHD) is the most important cause of mortality in patients receiving allogeneic hematopoietic stem cell transplantation. Given that it occurs at the stage of severe tissue damage, its diagnosis is la...
We investigated the impact of donor characteristics on outcomes in allogeneic hematopoietic cell transplantation (HCT) recipients using a novel machine learning approach, the Nonparametric Failure Time Bayesian Additive Regression Trees (NFT BART). N...
Stem Cells (SCs) show a wide range of applications in the treatment of numerous diseases, including neurodegenerative diseases, diabetes, cardiovascular diseases, cancer, etc. SC related research has gained popularity owing to the unique characterist...
Machine learning techniques are widely used nowadays in the healthcare domain for the diagnosis, prognosis, and treatment of diseases. These techniques have applications in the field of hematopoietic cell transplantation (HCT), which is a potentially...