Zobrazeno 1 - 10
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pro vyhledávání: '"Orfanoudaki, A"'
Identifying the causal pathways of unfairness is a critical objective in improving policy design and algorithmic decision-making. Prior work in causal fairness analysis often requires knowledge of the causal graph, hindering practical applications in
Externí odkaz:
http://arxiv.org/abs/2405.14848
Machine learning algorithms have grown in sophistication over the years and are increasingly deployed for real-life applications. However, when using machine learning techniques in practical settings, particularly in high-risk applications such as me
Externí odkaz:
http://arxiv.org/abs/2310.03545
Autor:
Orfanoudaki, Agni
Over the past decades, analytics have provided the promise of revolutionizing healthcare, providing more effective, patient-centered, and personalized care. As an increasing amount of data is being collected, computational performance is improved, an
Autor:
Orfanoudaki, Georgia1,2 (AUTHOR) orfanoudaki@gmail.com, Psatha, Konstantina1,2,3 (AUTHOR) kpsatha@auth.gr, Aivaliotis, Michalis1,2,4,5 (AUTHOR) aivaliotis@auth.gr
Publikováno v:
International Journal of Molecular Sciences. Jul2024, Vol. 25 Issue 13, p7298. 25p.
Autor:
Bertsimas, Dimitris, Orfanoudaki, Agni
As machine learning algorithms start to get integrated into the decision-making process of companies and organizations, insurance products are being developed to protect their owners from liability risk. Algorithmic liability differs from human liabi
Externí odkaz:
http://arxiv.org/abs/2106.00839
Publikováno v:
International Journal of Molecular Sciences, Vol 25, Iss 13, p 7298 (2024)
Mantle cell lymphoma (MCL) is a rare, incurable, and aggressive B-cell non-Hodgkin lymphoma (NHL). Early MCL diagnosis and treatment is critical and puzzling due to inter/intra-tumoral heterogeneity and limited understanding of the underlying molecul
Externí odkaz:
https://doaj.org/article/7c7325c17d814b4f88a1c3db910cb9ad
Tree-based models are increasingly popular due to their ability to identify complex relationships that are beyond the scope of parametric models. Survival tree methods adapt these models to allow for the analysis of censored outcomes, which often app
Externí odkaz:
http://arxiv.org/abs/2012.04284
Autor:
Bertsimas, Dimitris, Boussioux, Léonard, Wright, Ryan Cory, Delarue, Arthur, Digalakis Jr., Vassilis, Jacquillat, Alexandre, Kitane, Driss Lahlou, Lukin, Galit, Li, Michael Lingzhi, Mingardi, Luca, Nohadani, Omid, Orfanoudaki, Agni, Papalexopoulos, Theodore, Paskov, Ivan, Pauphilet, Jean, Lami, Omar Skali, Stellato, Bartolomeo, Bouardi, Hamza Tazi, Carballo, Kimberly Villalobos, Wiberg, Holly, Zeng, Cynthia
The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to s
Externí odkaz:
http://arxiv.org/abs/2006.16509
Current clinical practice guidelines for managing Coronary Artery Disease (CAD) account for general cardiovascular risk factors. However, they do not present a framework that considers personalized patient-specific characteristics. Using the electron
Externí odkaz:
http://arxiv.org/abs/1910.08483
State-of-the-art clustering algorithms use heuristics to partition the feature space and provide little insight into the rationale for cluster membership, limiting their interpretability. In healthcare applications, the latter poses a barrier to the
Externí odkaz:
http://arxiv.org/abs/1812.00539