Clinical pathway analysis using graph-based approach and Markov models

Cluster analysis is one of the most important aspects in the data mining process for discovering groups and identifying interesting distributions or patterns over the considered data sets. A new method for sequences clustering and prediction is presented in this paper, which is based on a hybrid model that uses our b-coloring based clustering approach as well as Markov chain models. The paper focuses on clinical pathway analysis but the method applies to every kind of sequences, and a generic decision support framework has been developed for managers and experts. The interesting result is that the clusters obtained have a twofold representation. Firstly, there is a set of dominant sequences which reflects the properties of the cluster and also guarantees that clusters are well separated within the partition. On the other hand, the behavior of each cluster is governed by a finite-state Markov chain model which allows probabilistic prediction. These models can be used for predicting possible paths for a new patient, and for helping medical professionals to eventually react to exceptions during the clinical process.