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The automatic discovery of a model to represent the history of encounters ofa group of patients with the healthcare system -- the so-called "pathway ofpatients" -- is a new field of research that supports clinical andorganisational decisions to improve the quality and efficiency of the treatmentprovided. The pathways of patients with chronic conditions tend to varysignificantly from one person to another, have repetitive tasks, and demand theanalysis of multiple perspectives (interventions, diagnoses, medicalspecialities, among others) influencing the results. Therefore, modelling andmining those pathways is still a challenging task. In this work, we propose aframework comprising: (i) a pathway model based on a multi-aspect graph, (ii) anovel dissimilarity measurement to compare pathways taking the elapsed timeinto account, and (iii) a mining method based on traditional centralitymeasures to discover the most relevant steps of the pathways. We evaluated theframework using the study cases of pregnancy and diabetes, which revealed itsusefulness in finding clusters of similar pathways, representing them in aneasy-to-interpret way, and highlighting the most significant patterns accordingto multiple perspectives.

Framework based on complex networks to model and mine patient pathways