Corrigendum to Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression: [Computer Methods and Programs in Biomedicine, Volume 208, (September 2021) 106180

  1. De Brouwer, E.
  2. Becker, T.
  3. Moreau, Y.
  4. Havrdova, E.K.
  5. Trojano, M.
  6. Eichau, S.
  7. Ozakbas, S.
  8. Onofrj, M.
  9. Grammond, P.
  10. Kuhle, J.
  11. Kappos, L.
  12. Sola, P.
  13. Cartechini, E.
  14. Lechner-Scott, J.
  15. Alroughani, R.
  16. Gerlach, O.
  17. Kalincik, T.
  18. Granella, F.
  19. Grand'Maison, F.
  20. Bergamaschi, R.
  21. Sá, M.J.
  22. Van Wijmeersch, B.
  23. Soysal, A.
  24. Sanchez-Menoyo, J.L.
  25. Solaro, C.
  26. Boz, C.
  27. Iuliano, G.
  28. Buzzard, K.
  29. Aguera-Morales, E.
  30. Terzi, M.
  31. Trivio, T.C.
  32. Spitaleri, D.
  33. Van Pesch, V.
  34. Shaygannejad, V.
  35. Moore, F.
  36. Oreja-Guevara, C.
  37. Maimone, D.
  38. Gouider, R.
  39. Csepany, T.
  40. Ramo-Tello, C.
  41. Peeters, L.
  42. Montrer des auteurs +
Revue:
Computer methods and programs in biomedicine

ISSN: 1872-7565

Année de publication: 2022

Volumen: 213

Pages: 106479

Type: Erreur

DOI: 10.1016/J.CMPB.2021.106479 GOOGLE SCHOLAR lock_openAccès ouvert editor
La source de données: Scopus