Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Jean-Baptiste Excoffier"'
Autor:
Carl-Joe Mehanna, Eric H Souied, Alexandra Miere, Francesca Amoroso, Elie Abitbol, Jean-Baptiste Excoffier, Samuel Kerr, Matthieu Ortala
Publikováno v:
BMJ Open Ophthalmology, Vol 7, Iss 1 (2022)
Objective To assess the ability of a deep learning model to distinguish between diabetic retinopathy (DR), sickle cell retinopathy (SCR), retinal vein occlusions (RVOs) and healthy eyes using ultra-widefield colour fundus photography (UWF-CFP).Method
Externí odkaz:
https://doaj.org/article/b048a272f38f459d98082a483dcf1c2f
Autor:
Camille Jung, Jean-Baptiste Excoffier, Mathilde Raphaël-Rousseau, Noémie Salaün-Penquer, Matthieu Ortala, Christos Chouaid
Publikováno v:
PLoS ONE, Vol 17, Iss 2, p e0263266 (2022)
Characteristics of patients at risk of developing severe forms of COVID-19 disease have been widely described, but very few studies describe their evolution through the following waves. Data was collected retrospectively from a prospectively maintain
Externí odkaz:
https://doaj.org/article/2498146253a145eaa3c1447f2ebe24bf
Autor:
Léna Bardet, Jean-Baptiste Excoffier, Noemie Salaun-Penquer, Matthieu Ortala, Maud Pasquier, Emmanuelle Mathieu d'Argent, Nathalie Massin
Publikováno v:
Reproductive BioMedicine Online. 45:246-255
Can a machine learning model better predict the cumulative live birth rate for a couple after intrauterine insemination or embryo transfer than Cox regression based on their personal characteristics?Retrospective cohort study conducted in two French
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c5b21295d236e733ab2405f7adbaf6a
https://doi.org/10.1016/j.rbmo.2022.03.020
https://doi.org/10.1016/j.rbmo.2022.03.020
Analysis of COVID-19 inpatients in France during first lockdown of 2020 using explainability methods
Autor:
Jean-Baptiste Excoffier, Noémie Salaün-Penquer, Matthieu Ortala, Mathilde Raphaël-Rousseau, Christos Chouaid, Camille Jung
Publikováno v:
Medical & Biological Engineering & Computing. 60:1647-1658
The COVID-19 pandemic rapidly puts a heavy pressure on hospital centers, especially on intensive care units. There was an urgent need for tools to understand typology of COVID-19 patients and identify those most at risk of aggravation during their ho
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f82cc5837720c98c80f45acf4d354b5d
https://doi.org/10.1007/s11517-022-02540-0
https://doi.org/10.1007/s11517-022-02540-0
Decision support tools in healthcare require a strong confidence in the developed Machine Learning (ML) models both in terms of performances and in their ability to provide users a deeper understanding of the underlying situation. This study presents
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3ef879f934e47b29f1bec12d682435e5
https://doi.org/10.3233/shti220520
https://doi.org/10.3233/shti220520
Publikováno v:
Studies in health technology and informatics. 294
Decision support tools in healthcare require a strong confidence in the developed Machine Learning (ML) models both in terms of performances and in their ability to provide users a deeper understanding of the underlying situation. This study presents
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::7a553d83ba1b5e2e5f6175e753053b06
https://pubmed.ncbi.nlm.nih.gov/35612141
https://pubmed.ncbi.nlm.nih.gov/35612141
Autor:
Camille Jung, Jean-Baptiste Excoffier, Mathilde Raphaël-Rousseau, Noémie Salaün-Penquer, Matthieu Ortala, Christos Chouaid
Publikováno v:
PloS one. 17(2)
Characteristics of patients at risk of developing severe forms of COVID-19 disease have been widely described, but very few studies describe their evolution through the following waves. Data was collected retrospectively from a prospectively maintain
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7dfb58c7641cc15f5212291ff2ff8e7b
https://pubmed.ncbi.nlm.nih.gov/35192649
https://pubmed.ncbi.nlm.nih.gov/35192649
Autor:
Gabriel Ferrettini, Chantal Soulé-Dupuy, Julien Aligon, Jean-Baptiste Excoffier, Elodie Escriva
Publikováno v:
Information Systems Frontiers
Information Systems Frontiers, Springer Verlag, 2021, pp.1-31. ⟨10.1007/s10796-021-10141-9⟩
Information Systems Frontiers, Springer Verlag, 2021, pp.1-31. ⟨10.1007/s10796-021-10141-9⟩
As Machine Learning (ML) is now widely applied in many domains, in both research and industry, an understanding of what is happening inside the black box is becoming a growing demand, especially by non-experts of these models. Several approaches had
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::77ef4c02fee244a3aa7ded443b0e731f
https://hal.archives-ouvertes.fr/hal-03259008/document
https://hal.archives-ouvertes.fr/hal-03259008/document
Autor:
Elie Abitbol, Alexandra Miere, Jean-Baptiste Excoffier, Carl-Joe Mehanna, Francesca Amoroso, Samuel Kerr, Matthieu Ortala, Eric H Souied
Publikováno v:
BMJ Open Ophthalmology, Vol 7, Iss 1 (2022)
ObjectiveTo assess the ability of a deep learning model to distinguish between diabetic retinopathy (DR), sickle cell retinopathy (SCR), retinal vein occlusions (RVOs) and healthy eyes using ultra-widefield colour fundus photography (UWF-CFP).Methods
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e2ab6e6e65ae4049b236bce59cd49321
https://doi.org/10.1136/bmjophth-2021-000924
https://doi.org/10.1136/bmjophth-2021-000924