Zobrazeno 1 - 10
of 266
pro vyhledávání: '"Vidal, Maria Esther"'
Autor:
Fahimi, Miriam, Russo, Mayra, Scott, Kristen M., Vidal, Maria-Esther, Berendt, Bettina, Kinder-Kurlanda, Katharina
The field of fair AI aims to counter biased algorithms through computational modelling. However, it faces increasing criticism for perpetuating the use of overly technical and reductionist methods. As a result, novel approaches appear in the field to
Externí odkaz:
http://arxiv.org/abs/2407.16496
Autor:
Krithara, Anastasia, Aisopos, Fotis, Rentoumi, Vassiliki, Nentidis, Anastasios, Bougatiotis, Konstantinos, Vidal, Maria-Esther, Menasalvas, Ernestina, Rodriguez-Gonzalez, Alejandro, Samaras, Eleftherios G., Garrard, Peter, Torrente, Maria, Pulla, Mariano Provencio, Dimakopoulos, Nikos, Mauricio, Rui, De Argila, Jordi Rambla, Tartaglia, Gian Gaetano, Paliouras, George
Publikováno v:
2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), Cordoba, Spain, 2019, pp. 106-111
The vision of IASIS project is to turn the wave of big biomedical data heading our way into actionable knowledge for decision makers. This is achieved by integrating data from disparate sources, including genomics, electronic health records and bibli
Externí odkaz:
http://arxiv.org/abs/2407.06748
Autor:
Russo, Mayra, Vidal, Maria-Esther
Machine Learning (ML) systems are capable of reproducing and often amplifying undesired biases. This puts emphasis on the importance of operating under practices that enable the study and understanding of the intrinsic characteristics of ML pipelines
Externí odkaz:
http://arxiv.org/abs/2407.00509
Autor:
Benítez-Andrades, José Alberto, Alija-Pérez, José-Manuel, Vidal, Maria-Esther, Pastor-Vargas, Rafael, García-Ordás, María Teresa
Publikováno v:
JMIR Medical Informatics, Volume 10, Issue 2, 2022, ID e34492
Background: Eating disorders are increasingly prevalent, and social networks offer valuable information. Objective: Our goal was to identify efficient machine learning models for categorizing tweets related to eating disorders. Methods: Over three mo
Externí odkaz:
http://arxiv.org/abs/2402.05571
Autor:
Benítez-Andrades, José Alberto, García-Ordás, María Teresa, Russo, Mayra, Sakor, Ahmad, Rotger, Luis Daniel Fernandes, Vidal, Maria-Esther
Publikováno v:
Semantic Web, Volume 4, Issue 5, pp. 873-892, 2023
Social networks are vital for information sharing, especially in the health sector for discussing diseases and treatments. These platforms, however, often feature posts as brief texts, posing challenges for Artificial Intelligence (AI) in understandi
Externí odkaz:
http://arxiv.org/abs/2402.05536
Autor:
Ibáñez, Luis-Daniel, Domingue, John, Kirrane, Sabrina, Seneviratne, Oshani, Third, Aisling, Vidal, Maria-Esther
Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making and a wide range of Artificial Intelligence (AI) services across major corporations such as Google, Walmart, and AirBnb. KGs complement Machine Lear
Externí odkaz:
http://arxiv.org/abs/2310.19503
In recent years, there have been valuable efforts and contributions to make the process of RDF knowledge graph creation traceable and transparent; extending and applying declarative mapping languages is an example. One challenging step is the traceab
Externí odkaz:
http://arxiv.org/abs/2210.15645
Autor:
Sakor, Ahmad, Jozashoori, Samaneh, Niazmand, Emetis, Rivas, Ariam, Bougiatiotis, Kostantinos, Aisopos, Fotis, Iglesias, Enrique, Rohde, Philipp D., Padiya, Trupti, Krithara, Anastasia, Paliouras, Georgios, Vidal, Maria-Esther
In this paper, we present Knowledge4COVID-19, a framework that aims to showcase the power of integrating disparate sources of knowledge to discover adverse drug effects caused by drug-drug interactions among COVID-19 treatments and pre-existing condi
Externí odkaz:
http://arxiv.org/abs/2206.07375
Knowledge Graphs (KGs) integrate heterogeneous data, but one challenge is the development of efficient tools for allowing end users to extract useful insights from these sources of knowledge. In such a context, reducing the size of a Resource Descrip
Externí odkaz:
http://arxiv.org/abs/2205.13883
Autor:
Calvo, Virginia, Niazmand, Emetis, Carcereny, Enric, Rodriguez-Abreu, Delvys, Cobo, Manuel, López-Castro, Rafael, Guirado, María, Camps, Carlos, Laura Ortega, Ana, Bernabé, Reyes, Massutí, Bartomeu, Garcia-Campelo, Rosario, del Barco, Edel, Luis González-Larriba, José, Bosch-Barrera, Joaquim, Martínez, Marta, Torrente, María, Vidal, María-Esther, Provencio, Mariano
Publikováno v:
In Lung Cancer September 2024 195
