Work

Triadic concept analysis for insights extraction from longitudinal studies in health

Year: 2024

Type: article

Abstract: In the health field, longitudinal studies involve the recording of clinical observations of the same sample of patients over successive periods, referred to as waves. This type of database serves as a valuable source of information and insights, particularly when examining the temporal aspect, allowing the extraction of relevant and non-obvious knowledge. The triadic concept analysis theory has been proposed to describe the ternary relationships between objects, attributes, and conditions. In this study, we present a methodology for exploring longitudinal health databases using both the triadic theory and triadic rules, which are similar to association rules but incorporate temporal relations. Through four case studies , we demonstrate the potential of applying triadic analysis to longitudinal databases to identify risk patterns, enhance decision-making processes, and deepen our understanding of temporal dynamics. These findings suggest a promising approach for describing longitudinal databases and obtaining insights to improve clinical decision-support systems for disease treatment.

Source: Data Science and Management

DOI: 10.1016/j.dsm.2024.10.001

Authors: João Pedro Santos, Atílio Ferreira Silva, Henrique Fernandes Viana Mendes, Mark Alan Junho Song, Luis Enrique Zárate

Institution: Pontifícia Universidade Católica de Minas Gerais

Language: English

Topic: Data Mining Algorithms and Applications

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: diamond

Funders: Pontifícia Universidade Católica de Minas Gerais, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado de Minas Gerais