Defesa de Tese de Doutorado: Complex Networks to Model and Mine Patient Pathways

Hora: 09h

Orientadores:
Antônio Tadeu Azevedo Gomes - Laboratório Nacional de Computação Científica - LNCC
Alex Borges Vieira - Universidade Federal de Juiz de Fora - UFJF
Marcia Ito - Centro Paula Souza - CPS

Banca Examinadora:
Antônio Tadeu Azevedo Gomes - Laboratório Nacional de Computação Científica - LNCC (presidente)
Fabio André Machado Porto - Laboratório Nacional de Computação Científica - LNCC
Agma Juci Machado Traina - USP
Deborah Ribeiro Carvalho - PUCPR

Suplentes:
Regina Célia Cerqueira de Almeida - Laboratório Nacional de Computação Científica - LNCC
Débora Christina Muchaluat Saade - Universidade Federal Fluminense - UFF

Resumo:

The use of healthcare data to rebuild the steps patients follo wed during their treatment - the pathway of
patients - is a helpful tool in a variety of scenarios. Examples include inspecting whether clinical guidelines are working as expected, identifying whether there are groups of patients with similar disease patterns, and assessing whether health resources distribution is appropriate. The automatic discovery of patient pathways is a growing field of research that records approaches based on different techniques, such as sequence mining, stochastic modelling, and process mining. Despite the advancements in the area, there are still challenges, especially when modelling and mining specific types of pathways, such as those associated with chronic conditions and health maintenance. These types of pathways demand methods to deal with encounters that repeat themselves, to support multiple perspectives (interventions, diagnoses, medical specialities, among others) influencing the results, and to keep time information between the encounters. This thesis proposes a framework to deal with such pathways. It comprises (i) a pathway model based on a multi-aspect graph, (ii) a dissimilarity function to compare pathways while 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 case studies of pregnancy and diabetes. They revealed the usefulness of the framework in finding clusters of similar pathways, representing them in an easy-to-interpret way and highlighting the most significant patterns according to multiple perspectives.