Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Valérie Bleret"'
Autor:
Lorena González-Castro, Marcela Chávez, Patrick Duflot, Valérie Bleret, Guilherme Del Fiol, Martín López-Nores
Publikováno v:
Applied Sciences, Vol 14, Iss 13, p 5909 (2024)
Accurate and early prediction of breast cancer recurrence is crucial to guide medical decisions and treatment success. Machine learning (ML) has shown promise in this domain. However, its effectiveness critically depends on proper hyperparameter sett
Externí odkaz:
https://doaj.org/article/d0fe706f11dd46c6b5f4bd7b48036b64
Autor:
Izidor Mlakar, Simon Lin, Ilona Aleksandraviča, Krista Arcimoviča, Jānis Eglītis, Mārcis Leja, Ángel Salgado Barreira, Jesús G. Gómez, Mercedes Salgado, Jesús G. Mata, Doroteja Batorek, Matej Horvat, Maja Molan, Maja Ravnik, Jean-François Kaux, Valérie Bleret, Catherine Loly, Didier Maquet, Elena Sartini, Urška Smrke
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-14 (2021)
Abstract Background It is encouraging to see a substantial increase in individuals surviving cancer. Even more so since most of them will have a positive effect on society by returning to work. However, many cancer survivors have unmet needs, especia
Externí odkaz:
https://doaj.org/article/819c5f68919449a4a12794ae9f12fe8d
Autor:
Lorena González-Castro, Marcela Chávez, Patrick Duflot, Valérie Bleret, Alistair G. Martin, Marc Zobel, Jama Nateqi, Simon Lin, José J. Pazos-Arias, Guilherme Del Fiol, Martín López-Nores
Publikováno v:
Cancers, Vol 15, Iss 10, p 2741 (2023)
Recurrence is a critical aspect of breast cancer (BC) that is inexorably tied to mortality. Reuse of healthcare data through Machine Learning (ML) algorithms offers great opportunities to improve the stratification of patients at risk of cancer recur
Externí odkaz:
https://doaj.org/article/394978412f144e97b25ec20ffd98cc52
Autor:
Izidor Mlakar, Simon Lin, Jama Nateqi, Stefanie Gruarin, Lorena Diéguez, Paulina Piairo, Liliana R. Pires, Sara Tement, Ilona Aleksandraviča, Mārcis Leja, Krista Arcimoviča, Valérie Bleret, Jean-François Kaux, Philippe Kolh, Didier Maquet, Jesús Garcia Gómez, Jesus García Mata, Mercedes Salgado, Matej Horvat, Maja Ravnik, Vojko Flis, Urška Smrke
Publikováno v:
Journal of Clinical Medicine, Vol 11, Iss 7, p 2041 (2022)
(1) Background: The needs of cancer survivors are often not reflected in practice. One of the main barriers of the use of patient-reported outcomes is associated with data collection and the interpretation of patient-reported outcomes (PROs) due to a
Externí odkaz:
https://doaj.org/article/ffe20860a6f24264af3dba0152e1efff
Autor:
Gaetano Manzo, Yvan Pannatier, Patrick Duflot, Philippe Kolh, Marcela Chavez, Valérie Bleret, Davide Calvaresi, Oscar Jimenez-del-Toro, Michael Schumacher, Jean-Paul Calbimonte
Publikováno v:
Computer Methods and Programs in Biomedicine. 231:107373
Autor:
Mārcis Leja, Ángel Salgado Barreira, Valérie Bleret, Ilona Aleksandraviča, Simon Lin, Jesús García Mata, Maja Ravnik, Maja Molan, Jean-François Kaux, Jānis Eglītis, Urška Smrke, Matej Horvat, Jesús García Gómez, Catherine Loly, Mercedes Salgado, Elena Sartini, Krista Arcimoviča, Doroteja Batorek, Izidor Mlakar, Didier Maquet
Publikováno v:
BMC Medical Informatics and Decision Making
BMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-14 (2021)
BMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-14 (2021)
BackgroundIt is encouraging to see a substantial increase in individuals surviving cancer. Even more so since most of them will have a positive effect on society by returning to work. However, many cancer survivors have unmet needs, especially when i