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