Pulled from PubMed
Neta Gotlieb 1,2, Amirhossein Azhie 1, Divya Sharma 3, Ashley Spann 4, Nan-Ji Suo 5, Jason Tran 1, Ani Orchanian-Cheff 6, Bo Wang 7, Anna Goldenberg 7, Michael Chassé 8,9, Heloise Cardinal 9,10, Joseph Paul Cohen 9,11,12, Andrea Lodi 9,13, Melanie Dieude 9,10,14,15, Mamatha Bhat 1,9,16,
Solid-organ transplantation is a life-saving treatment for end-stage organ disease in highly selected patients. Alongside the tremendous progress in the last several decades, new challenges have emerged. The growing disparity between organ demand and supply requires optimal patient/donor selection and matching. Improvements in long-term graft and patient survival require data-driven diagnosis and management of post-transplant complications. The growing abundance of clinical, genetic, radiologic, and metabolic data in transplantation has led to increasing interest in applying machine-learning (ML) tools that can uncover hidden patterns in large datasets. ML algorithms have been applied in predictive modeling of waitlist mortality, donor-recipient matching, survival prediction, post-transplant complications diagnosis, and prediction, aiming to optimize immunosuppression and management. In this review, we provide insight into the various applications of ML in transplant medicine, why these were used to evaluate a specific clinical question, and the potential of ML to transform the care of transplant recipients.