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Introducing Machine Learning for full MS patient trajectories improves predictions for disability score progression

dc.contributor.authorAguera-Morales, E.
dc.contributor.authorLechner-Scott, J.
dc.contributor.authorKuhle, J.
dc.contributor.authorSanchez Menoyo, J. L.
dc.contributor.authorRojas, J. I.
dc.contributor.authorPrevost, J.
dc.contributor.authorOnofrj, M.
dc.contributor.authorRio, M. E.
dc.contributor.authorSa, M. J.
dc.contributor.authorSaladino, M. L.
dc.contributor.authorSlee, M.
dc.contributor.authorBarnett, M.
dc.contributor.authorTerzi, M.
dc.contributor.authorDeri, N.
dc.contributor.authorMcCombe, P.
dc.contributor.authorSola, P.
dc.contributor.authorDuquette, P.
dc.contributor.authorGrammond, P.
dc.contributor.authorAmpapa, R.
dc.contributor.authorAlroughani, R.
dc.contributor.authorHupperts, R.
dc.contributor.authorTurkoglu, R.
dc.contributor.authorGouider, R.
dc.contributor.authorFernandez Bolanos, R.
dc.contributor.authorBergamaschi, R.
dc.contributor.authorKalincik, T.
dc.contributor.authorMoreau, Y.
dc.contributor.authorAltintas, A.
dc.contributor.authorDe Brouwer, E.
dc.contributor.authorPeeters, L.
dc.contributor.authorBecker, T.
dc.contributor.authorSoysal, A.
dc.contributor.authorVan Wijmeersch, B.
dc.contributor.authorBoz, C.
dc.contributor.authorOreja-Guevara, C.
dc.contributor.authorGobbi, C.
dc.contributor.authorSolaro, C.
dc.contributor.authorRamo, C.
dc.contributor.authorSpitaleri, D. L.
dc.contributor.authorMaimone, D.
dc.contributor.authorCartechini, E.
dc.contributor.authorButler, E.
dc.contributor.authorHavrdova, E.
dc.contributor.authorPatti, F.
dc.contributor.authorGranella, F.
dc.contributor.authorGrand'Maison, F.
dc.contributor.authorMoore, F.
dc.contributor.authorVerheul, F.
dc.contributor.authorIuliano, G.
dc.contributor.authorButzkueven, H.
dc.date.accessioned2021-03-03T09:04:31Z
dc.date.available2021-03-03T09:04:31Z
dc.identifier.citationDe Brouwer E., Peeters L., Becker T., Altintas A., Soysal A., Van Wijmeersch B., Boz C., Oreja-Guevara C., Gobbi C., Solaro C., et al., "Introducing Machine Learning for full MS patient trajectories improves predictions for disability score progression", 35th Congress of the European-Committee-for-Treatment-and-Research-in-Multiple-Sclerosis (ECTRIMS) / 24th Annual Conference of Rehabilitation in MS, Stockholm, İsveç, 11 - 13 Eylül 2019, cilt.25, ss.63-65
dc.identifier.otherav_1b1490f7-48e1-4621-a9f0-cd466dc4d675
dc.identifier.othervv_1032021
dc.identifier.urihttp://hdl.handle.net/20.500.12627/23495
dc.language.isoeng
dc.subjectKlinik Tıp (MED)
dc.subjectSağlık Bilimleri
dc.subjectDahili Tıp Bilimleri
dc.subjectNöroloji
dc.subjectYaşam Bilimleri
dc.subjectTemel Bilimler
dc.subjectKLİNİK NEUROLOJİ
dc.subjectKlinik Tıp
dc.subjectNEUROSCIENCES
dc.subjectSinirbilim ve Davranış
dc.subjectYaşam Bilimleri (LIFE)
dc.subjectTıp
dc.titleIntroducing Machine Learning for full MS patient trajectories improves predictions for disability score progression
dc.typeBildiri
dc.contributor.departmentİstanbul Üniversitesi , ,
dc.identifier.volume25
dc.contributor.firstauthorID157166


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