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Machine Learning Approaches to Surrogate Multifidelity Growth and Remodeling Models for Efficient Abdominal Aortic Aneurysmal Applications

Computational Growth and Remodeling (G&R) models have been widely used to capture the pathological development of arterial diseases and have shown promise for aiding clinical diagnosis, prognosis prediction, and staging classification. However, due to the high complexity of the arterial adaptati...

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Vydáno v:Comput Biol Med
Hlavní autoři: Jiang, Zhenxiang, Choi, Jongeun, Baek, Seungik
Médium: Artigo
Jazyk:Inglês
Vydáno: 2021
Témata:
On-line přístup:https://ncbi.nlm.nih.gov/pmc/articles/PMC8169625/
https://ncbi.nlm.nih.gov/pubmed/34015599
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.compbiomed.2021.104394
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