Framework based on complex networks to model and mine patient pathways

Caroline de Oliveira Costa Souza Rosa, Márcia Ito, Alex Borges Vieira, Klaus Wehmuth, Antônio Tadeu Azevedo Gomes
9/25/2023
cs.CYcs.AIcs.IR

Official code: caroline-rosa/framework_patient_pathways (1 star, last commit 2025-04-09).

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.

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Code Implementations(1)

caroline-rosa/framework_patient_pathwaysOfficial100%

This project implements the framework proposed in https://doi.org/10.48550/arXiv.2309.14208 for assessing multi-perspective patient pathways.

10Python, Jupyter NotebookOct 18, 20231 years ago
patient-pathwaysprocess-mining

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Cite this paper

@article{rosa2023framework,
  title  = {Framework based on complex networks to model and mine patient pathways},
  author = {Caroline de Oliveira Costa Souza Rosa and Márcia Ito and Alex Borges Vieira and Klaus Wehmuth and Antônio Tadeu Azevedo Gomes},
  year   = {2023},
  eprint = {2309.14208},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2309.14208v2}
}

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