Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression

  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. José Sá, M.
  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 0169-2607

Argitalpen urtea: 2021

Alea: 208

Mota: Artikulua

DOI: 10.1016/J.CMPB.2021.106180 GOOGLE SCHOLAR