Interpretability is crucial for doctors, hospitals, pharmaceutical companies and biotechnology corporations to analyze and make decisions for high stakes problems that involve human health. Tree-based methods have been widely adopted for \textit{survival analysis} due to their appealing interpretablility and their ability to capture complex relationships. However, most existing methods to produce survival trees rely on heuristic (or greedy) algorithms, which risk producing sub-optimal models. We present a dynamic-programming-with-bounds approach that finds provably-optimal sparse survival tree models, frequently in only a few seconds.

In a statement, CLDC says that it will work with Rui Xin to develop a consumer financial platform. CLDC expects to provide value-added consumer financial services to insurance consumers of Rui Xin and its partners. In addition, CLDC and Rui Xin will explore opportunities for collaboration in areas such as insurance consumer acquisition, development of insurance products, expansion of insurance business, and customisation of consumer financial solutions. Moreover, CLDC will benefit from Rui Xin and its partners' advanced technological capabilities in big data and artificial intelligence to improve its risk management and enhance its customer experience. In its turn, Rui Xin will be able to explore new business opportunities and increase its competency to eventually expand its customer base in the insurance industry by benefiting from CLDC's financial service expertise, bank credit facility resources, and client base in certain regional markets.