Descriptive Modelling of Clinical Conditions with Data-driven Rule Mining in Physiological Data

This paper presents an approach to automatically mine rules in time series data representing physiological parameters in clinical conditions. The approach is fully data driven, where prototypical patterns are mined for each physiological time series data. The generated rules based on the prototypical patterns are then described in a textual representation which captures trends in each physiological parameter and their relation to the other physiological data. In this paper, a method for measuring similarity of rule sets is introduced in order to validate the uniqueness of rule sets. This method is evaluated on physiological records from clinical classes in the MIMIC online database such as angina, sepsis, respiratory failure, etc.. The results show that the rule mining technique is able to acquire a distinctive model for each clinical condition, and represent the generated rules in a human understandable textual representation.