Framework based on complex networks to model and mine patient pathways

TL;DR

This paper introduces a novel framework using complex networks to model and analyze patient pathways, enabling better understanding of healthcare processes and aiding clinical decision-making.

Contribution

It proposes a multi-aspect graph model, a new dissimilarity measure considering time, and a centrality-based mining method for patient pathway analysis.

Findings

Effective clustering of similar patient pathways.

Clear visualization of significant patterns.

Framework validated on pregnancy and diabetes cases.

Abstract

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.