Machine learning algorithm improves the detection of NASH (NAS-based) and at-risk NASH: A development and validation study - Sorbonne Université Access content directly
Journal Articles Hepatology Year : 2023

Machine learning algorithm improves the detection of NASH (NAS-based) and at-risk NASH: A development and validation study

Lauriane Le Collen
Brigitte Delemer
  • Function : Author
Christine Poitou
  • Function : Author
Nutrition et obésités: approches systémiques (UMR-S 1269)
Martine Vaxillaire
  • Function : Author
Bénédicte Toussaint
  • Function : Author
Aurélie Dechaume
  • Function : Author
Alaa Badreddine
  • Function : Author
Mathilde Boissel
  • Function : Author
Mehdi Derhourhi
  • Function : Author
Karine Clément
  • Function : Author
Nutrition et obésités: approches systémiques (UMR-S 1269)
Jean Petit
  • Function : Author
Frédéric Tran Mau-Them
  • Function : Author
Ange-Line Bruel
  • Function : Author
Christel Thauvin-Robinet
  • Function : Author
Alexandru Saveanu
  • Function : Author
Blandine Gatta Cherifi
  • Function : Author
Johanne Le Beyec-Le Bihan
  • Function : Author
Philippe Froguel
  • Function : Author
Amélie Bonnefond

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Life Sciences [q-bio]

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https://hal.sorbonne-universite.fr/hal-04153527

Submitted on : Thursday, July 6, 2023-11:29:19 AM

Last modification on : Friday, November 24, 2023-10:53:51 AM

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hal-04153527 , version 1 (06-07-2023)

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Lauriane Le Collen, Brigitte Delemer, Christine Poitou, Martine Vaxillaire, Bénédicte Toussaint, et al.. Machine learning algorithm improves the detection of NASH (NAS-based) and at-risk NASH: A development and validation study. Hepatology, 2023, 78 (1), pp.258-271. ⟨10.1016/j.gim.2023.100857⟩. ⟨hal-04153527⟩

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