Discrete Bayesian Network Interpretation of the Cox's Proportional Hazard Model

Cox’s Proportional Hazards (CPH) model is quite likely the most popular modeling technique in survival analysis. While the CPH model is able to represent relationships between a collection of risks and their common effect, Bayesian networks have become an attractive alter-native with far broader applications. Our paper focuses on a Bayesian network interpretation of the CPH model. We provide a method of en-coding knowledge from existing CPH models in the process of knowledge engineering for Bayesian networks. We compare the accuracy of the resulting Bayesian network to the CPH model, Kaplan-Meier estimate, and Bayesian network learned from data using the EM algorithm. Bayesian networks constructed from CPH model lead to much higher accuracy than other approaches, especially when the number of data records is very small