title: Prediction of antiepileptic drug treatment outcomes using machine learning.
abstract: Antiepileptic drug (AED) treatments produce inconsistent outcomes, often necessitating patients to go through several drug trials until a successful treatment can be found. This study proposes the use of machine learning techniques to predict epilepsy treatment outcomes of commonly used AEDs.
  
  
  
  Machine learning algorithms were trained and evaluated using features obtained from intracranial electroencephalogram (iEEG) recordings of the epileptiform discharges observed in Mecp2-deficient mouse model of the Rett Syndrome. Previous work have linked the presence of cross-frequency coupling (ICFC) of the delta (2-5 Hz) rhythm with the fast ripple (400-600 Hz) rhythm in epileptiform discharges. Using the ICFCto label post-treatment outcomes we compared support vector machines (SVMs) and random forest (RF) machine learning classifiers for providing likelihood scores of successful treatment outcomes.
  
  
  
  (a) There was heterogeneity in AED treatment outcomes, (b) machine learning techniques could be used to rank the efficacy of AEDs by estimating likelihood scores for successful treatment outcome, (c) ICFCfeatures yielded the most effective a priori identification of appropriate AED treatment, and (d) both classifiers performed comparably.
  
  
  
  Machine learning approaches yielded predictions of successful drug treatment outcomes which in turn could reduce the burdens of drug trials and lead to substantial improvements in patient quality of life.
authors.affiliation: Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S-3G4, Canada.
authors.forename: Sinisa
authors.initials: S
authors.lastname: Colic
authors.forename: Robert G
authors.initials: RG
authors.lastname: Wither
authors.forename: Min
authors.initials: M
authors.lastname: Lang
authors.forename: Liang
authors.initials: L
authors.lastname: Zhang
authors.forename: James H
authors.initials: JH
authors.lastname: Eubanks
authors.forename: Berj L
authors.initials: BL
authors.lastname: Bardakjian
mesh_terms: D000818:Animals; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004360:Drug Therapy, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000187:drug effects* / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis* / Q000188:drug therapy* / Q000503:physiopathology; D005260:Female; D000069550:Machine Learning; D051783:Methyl-CpG-Binding Protein 2 / Q000235:genetics; D051379:Mice; D018345:Mice, Knockout; D017063:Outcome Assessment, Health Care / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016896:Treatment Outcome
chemical_list: C497393:Mecp2 protein, mouse; D051783:Methyl-CpG-Binding Protein 2