Temporal data mining and process mining techniques to identify cardiovascular risk-associated clinical pathways in Type 2 diabetes patients

摘要

In this work we present the results of a workflow mining approach to analyze complex temporal datasets of Type 2 Diabetes (T2D) patients. The research has been performed within the EU project MOSAIC, which gathers T2D patients' data coming from three European hospitals and a local health care agency. The main idea underlying our approach is to use a suite of temporal data mining methods in order to derive healthcare pathways. The approach starts by processing raw data, derived from heterogeneous data sources, and create event logs, which contain meaningful healthcare activities. Once event logs have been obtained and tasks and transitions defined, it is possible to explore how state-of-art process mining techniques can be used to gain insights into T2D patients care. In the experimental section of this paper we illustrate the results of this approach applied to an integrated data repository containing clinical and administrative data of 1,020 T2D patients.