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Clinical databases store large amounts of information about patients and their medical conditions. Data mining techniques can extract relationships and patterns holding in this wealth of data, and thus be helpful in understanding the progression of diseases and the efficacy of the associated therapies. A typical structure of medical data is a sequence of observations of clinical parameters taken at different time moments. In this kind of contexts, the temporal dimension of data is a fundamental variable that should be taken in account in the mining process and returned as part of the extracted knowledge. Therefore, the classical and well established framework of sequential pattern mining is not enough, because it only focuses on the sequentiality of events, without extracting the typical time elapsing between two particular events. Time-annotated sequences (IAS), is a novel mining paradigm that solves this problem …