title: Computational classifiers for predicting the short-term course of Multiple sclerosis.
abstract: BACKGROUND
  The aim of this study was to assess the diagnostic accuracy (sensitivity and specificity) of clinical, imaging and motor evoked potentials (MEP) for predicting the short-term prognosis of multiple sclerosis (MS).
  
  
  METHODS
  We obtained clinical data, MRI and MEP from a prospective cohort of 51 patients and 20 matched controls followed for two years. Clinical end-points recorded were: 1) expanded disability status scale (EDSS), 2) disability progression, and 3) new relapses. We constructed computational classifiers (Bayesian, random decision-trees, simple logistic-linear regression-and neural networks) and calculated their accuracy by means of a 10-fold cross-validation method. We also validated our findings with a second cohort of 96 MS patients from a second center.
  
  
  RESULTS
  We found that disability at baseline, grey matter volume and MEP were the variables that better correlated with clinical end-points, although their diagnostic accuracy was low. However, classifiers combining the most informative variables, namely baseline disability (EDSS), MRI lesion load and central motor conduction time (CMCT), were much more accurate in predicting future disability. Using the most informative variables (especially EDSS and CMCT) we developed a neural network (NNet) that attained a good performance for predicting the EDSS change. The predictive ability of the neural network was validated in an independent cohort obtaining similar accuracy (80%) for predicting the change in the EDSS two years later.
  
  
  CONCLUSIONS
  The usefulness of clinical variables for predicting the course of MS on an individual basis is limited, despite being associated with the disease course. By training a NNet with the most informative variables we achieved a good accuracy for predicting short-term disability.
authors.affiliation: Department of Neuroscience, CIMA-University of Navarra, Pamplona, Spain.
authors.forename: Bartolome
authors.initials: B
authors.lastname: Bejarano
authors.forename: Mariangela
authors.initials: M
authors.lastname: Bianco
authors.forename: Dolores
authors.initials: D
authors.lastname: Gonzalez-Moron
authors.forename: Jorge
authors.initials: J
authors.lastname: Sepulcre
authors.forename: Joaquin
authors.initials: J
authors.lastname: Goñi
authors.forename: Juan
authors.initials: J
authors.lastname: Arcocha
authors.forename: Oscar
authors.initials: O
authors.lastname: Soto
authors.forename: Ubaldo
authors.initials: U
authors.lastname: Del Carro
authors.forename: Giancarlo
authors.initials: G
authors.lastname: Comi
authors.forename: Letizia
authors.initials: L
authors.lastname: Leocani
authors.forename: Pablo
authors.initials: P
authors.lastname: Villoslada
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004185:Disability Evaluation; D019054:Evoked Potentials, Motor / Q000502:physiology; D005260:Female; D006801:Humans; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009103:Multiple Sclerosis / Q000145:classification* / Q000175:diagnosis*; D009431:Neural Conduction / Q000502:physiology; D010525:Peripheral Nerves / Q000503:physiopathology; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D013997:Time Factors