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Orientador(es)
Resumo(s)
This study presents an analysis of data from patients with ascending thoracic aortic aneurysms (ATAAs). Two databases of 87 patients were available: one containing clinical variables and the other consisting of measurements of the maximum diameter taken along the ascending aorta. For the clinical database, both a supervised and an unsupervised learning method were applied to explore patterns within the data. On the other hand, for the ascending aorta dataset, experimental variograms were calculated, from which key parameters of interest were extracted. These parameters were then analyzed over time to assess temporal patterns. This analysis aimed to assess the emergence of similar patterns or behaviour in patients with aneurysms of comparable sizes. Based on the analyses conducted, the clinical variables with the greatest importance in surgical decision-making were identified, while the spatial statistical analysis revealed patterns that may be related to elasticity, stiffness, or deformations of the aorta
Descrição
Palavras-chave
Ascending aortic aneurysm Supervised and unsupervised methods Spatial statistics Variogram
Contexto Educativo
Citação
Oviedo Rodríguez, K., Carvalho, A., Valente, R., Xavier, J., & Tomás, A. C. (2026). Clinical Prediction and Spatial Statistical Analysis of Ascending Thoracic Aortic Aneurysm Structure. Mathematical and Computational Applications, 31(1), 10. https://doi.org/10.3390/mca31010010
Editora
MDPI