Time-Annotated Sequences for Medical Data Mining

Abstract

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 into 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. Recently defined in our laboratory [4] together with an efficient algorithm 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 assessing the effectiveness of the extracorporeal photopheresis (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 methodology for clinical data mining, whenever the time dimension is an important variable of the problem under investigation.