Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression.
Edward De BrouwerThijs BeckerYves MoreauEva Kubala HavrdovaMaria TrojanoSara EichauSerkan OzakbasMarco OnofrjPierre GrammondJens KuhleLudwig KapposPatrizia SolaElisabetta CartechiniJeannette Lechner-ScottRaed AlroughaniOliver GerlachTomas KalincikFranco GranellaFrancois Grand'MaisonRoberto BergamaschiMaria Jose SaBart Van WijmeerschAysun SoysalJose Luis Sanchez-MenoyoClaudio SolaroCavit BozGerardo IulianoKatherine BuzzardEduardo Aguera-MoralesMurat TerziTamara Castillo TrivioDaniele SpitaleriVincent Van PeschVahid ShaygannejadFraser MooreCelia Oreja GuevaraDavide MaimoneRiadh GouiderTunde CsepanyCristina Ramo-TelloLiesbet M. PeetersPublished in: Comput. Methods Programs Biomed. (2021)
Keyphrases
- machine learning
- cross sectional
- text mining
- explanation based learning
- pattern recognition
- decision trees
- data mining
- computer vision
- learning algorithm
- knowledge acquisition
- machine learning algorithms
- machine learning methods
- motion patterns
- inductive learning
- electronic health records
- predictive modeling
- disease progression
- clinical data
- inductive logic programming
- text classification
- information extraction
- knowledge discovery
- spatio temporal
- natural language
- feature selection