EsmamDS: A more diverse exceptional survival model mining approach
This work addresses the need for more informative and diverse tools in survival analysis, particularly for researchers in fields like medicine or epidemiology, though it appears incremental as it builds on an existing algorithm.
The authors tackled the problem of discovering factors associated with survival behavior by introducing EsmamDS, an algorithm that uses descriptive supervised pattern mining to identify subgroups with unusual survival models, building on Esmam to reduce redundancy and improve diversity in characterizations.
A variety of works in the literature strive to uncover the factors associated with survival behaviour. However, the computational tools to provide such information are global models designed to predict if or when a (survival) event will occur. When approaching the problem of explaining differences in survival behaviour, those approaches rely on (assumptions of) predictive features followed by risk stratification. In other words, they lack the ability to discover new information on factors related to survival. In contrast, we approach such a problem from the perspective of descriptive supervised pattern mining to discover local patterns associated with different survival behaviours. Hence, we introduce the EsmamDS algorithm: an Exceptional Model Mining framework to provide straightforward characterisations of subgroups presenting unusual survival models -- given by the Kaplan-Meier estimates. This work builds on the Esmam algorithm to address the problem of pattern redundancy and provide a more informative and diverse characterisation of survival behaviour.