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Akademický článek

Racial disparities in TAVR outcomes in patients with cancer

Autor: Ethan D. Kotloff, Yash Desai, Rohan Desai, Christopher Messner, Sergey Gnilopyat, Mark Sonbol, Abdullah Aljudaibi, Ai Tarui, Juwan Ives, Nisarg Shah, Ishan Vaish, Diljon Chahal, Brian Barr, Manu Mysore

Publikováno v: Frontiers in Cardiovascular Medicine, Vol 11 (2024)

BackgroundAdvances in cancer therapies and improvement in survival of cancer patients have led to a growing number of patients with both cancer and severe aortic stenosis (AS). Transcatheter aortic valve replacement (TAVR) has been shown to be a safe

Externí odkaz: https://doaj.org/article/f91debdf9f7e4db5967b270274fa83df

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Akademický článek

Hypertension, smoking, and preexistence of multiple cardiac risk factors correlate with carfilzomib-induced cardiovascular adverse events in a racially diverse population

Autor: Stacey Doran, Manu Mysore, Seyed Ebrahim Kassaian, Ethan Kotloff, Farin Kamangar, Ashkan Emadi, Jummai Apata, Brian Barr

Publikováno v: Frontiers in Cardiovascular Medicine, Vol 10 (2023)

BackgroundUse of the proteasome inhibitor carfilzomib has become a standard of care in patients with relapsed/refractory multiple myeloma. An association between carfilzomib and cardiovascular adverse events has been well documented, but this had not

Externí odkaz: https://doaj.org/article/3f68cefe570548df88e095fb998162aa

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Calibrate: Interactive Analysis of Probabilistic Model Output

Autor: Peter Xenopoulos, Joao Rulff, Luis Gustavo Nonato, Brian Barr, Claudio Silva

Publikováno v: IEEE Transactions on Visualization and Computer Graphics. 29:853-863

Analyzing classification model performance is a crucial task for machine learning practitioners. While practitioners often use count-based metrics derived from confusion matrices, like accuracy, many applications, such as weather prediction, sports b

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0dee82e7da111373ee8e9d985f15ce88
https://doi.org/10.1109/tvcg.2022.3209489

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Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Sets

Autor: Jun Yuan, Brian Barr, Kyle Overton, Enrico Bertini

Publikováno v: IEEE Transactions on Visualization and Computer Graphics. :1-18

One of the potential solutions for model interpretation is to train a surrogate model: a more transparent model that approximates the behavior of the model to be explained. Typically, classification rules or decision trees are used due to the intelli

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::56aea4396fc4b67c09141b5b6cc63a52
https://doi.org/10.1109/tvcg.2022.3219232

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