Intersection based generalization rules for the analysis of symbolic septic shock patient data

In intensive care units much data is irregularly recorded. Here, we consider the analysis of symbolic septic shock patient data. We show that it could be worth considering the generalization paradigm (individual cases generalized to more general rules) instead of the association paradigm (combining single attributes) when considering very individual cases (e.g. patients) and when expecting longer rules than shorter ones. We present an algorithm for rule generation and classification based on heuristically generated set-based intersections. We demonstrate the usefulness of our algorithm by analysing our septic shock patient data.