Framework based on complex networks to model and mine patient pathways
This work addresses the problem of improving healthcare quality and efficiency for patients with chronic conditions by automating pathway analysis, though it appears incremental in applying existing network methods to a specific domain.
The authors tackled the challenge of modeling and mining patient pathways in healthcare by proposing a framework using multi-aspect graphs, a novel dissimilarity measurement, and centrality-based mining, which was evaluated on pregnancy and diabetes cases to find clusters and highlight significant patterns.
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.