  The automatic discovery of a model to represent the history of encounters of
a group of patients with the healthcare system -- the so-called "pathway of
patients" -- is a new field of research that supports clinical and
organisational decisions to improve the quality and efficiency of the treatment
provided. The pathways of patients with chronic conditions tend to vary
significantly from one person to another, have repetitive tasks, and demand the
analysis of multiple perspectives (interventions, diagnoses, medical
specialities, among others) influencing the results. Therefore, modelling and
mining those pathways is still a challenging task. In this work, we propose a
framework comprising: (i) a pathway model based on a multi-aspect graph, (ii) a
novel dissimilarity measurement to compare pathways taking the elapsed time
into account, and (iii) a mining method based on traditional centrality
measures to discover the most relevant steps of the pathways. We evaluated the
framework using the study cases of pregnancy and diabetes, which revealed its
usefulness in finding clusters of similar pathways, representing them in an
easy-to-interpret way, and highlighting the most significant patterns according
to multiple perspectives.
