Time-annotated sequences for medical data mining

A typical structure of medical data is a sequence of ob- servations of clinical parameters taken at different time mo- ments. In this kind of contexts, the temporal dimension of data is a fundamental variable that should be taken into account in the mining process and returned as part of the extracted knowledge. Therefore, the classical and well es- tablished 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 ( TAS) is a novel mining paradigm that solves this problem. Recently defined in our laboratory [4] together with an efficient al- gorithm for extracting them, TAS are sequential patterns where each transition between two events is annotated with a typical transition time that is found frequent in the data. In this paper we report a real-world medical case study, in which the TAS mining paradigm is applied to clinical data regarding a set of patients in the follow-up of a liver transplantation. The aim of the data analysis is that of as- sessing the effectiveness of the extracorporeal photophere- sis (ECP) as a therapy to prevent rejection in solid organ transplantation. We believe that this case study does not only show the interestingness of extracting TAS patterns in this particular context but, more ambitiously, it suggests a general method- ology for clinical data mining, whenever the time dimension is an important variable of the problem under investigation.