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. Erakutsi egile guztiak +
Aldizkaria:
Computer methods and programs in biomedicine

ISSN: 1872-7565

Argitalpen urtea: 2022

Alea: 213

Orrialdeak: 106479

Mota: Hutsen zuzenketa

DOI: 10.1016/J.CMPB.2021.106479 GOOGLE SCHOLAR lock_openSarbide irekia editor

Garapen Iraunkorreko Helburuak

Datuen iturria: Scopus