讲座内容：

With the emergence of big data, statistical and data science approaches have significantly advanced health and medicine. Specifically, in the field of kidney disease, these methodologies have been applied to enhance prediction accuracy, improve patient outcomes, and optimise resource allocation. Kidney transplantation is the ultimate treatment for end-stage kidney disease patients. With the number of donated kidneys is far less than the number of patients waiting on the waiting list, it is challenging to allocate this scarce resource equally and efficiently. Additionally, with the individual difference in this transplant population, challenges arise for personalised healthcare. In my talk, I will provide an overview of several projects about using statistical and data science approaches to tackle these challenges in deceased donor kidney transplantation with the ultimate goal of providing a better life for patients. These projects include a simulation framework for deceased donor kidney allocation, a benchmark study for survival model performances, a personalised prediction modelling method for allograft and patient survival prediction, and a kidney transplant clinical support system. All together, these research findings contribute to the development of the allocation algorithm and provide clinical insights for monitoring and post-transplant early intervention for individual patients.