title: A statistical model for stroke outcome prediction and treatment planning.
abstract: Stroke is a major cause of mortality and long-term disability in the world. Predictive outcome models in stroke are valuable for personalized treatment, rehabilitation planning and in controlled clinical trials. We design a new multi-class classification model to predict outcome in the short-term, the putative therapeutic window for several treatments. Our model addresses the challenges of class imbalance, where the training data is dominated by samples of a single class, and highly correlated predictor and outcome variables, which makes learning the effects of treatments on the outcome difficult. Empirically our model outperforms the best-known previous predictive models and can infer the most effective treatments in improving outcome that have been independently validated in clinical studies.
authors.forename: Abhishek
authors.initials: A
authors.lastname: Sengupta
authors.forename: Vaibhav
authors.initials: V
authors.lastname: Rajan
authors.forename: Sakyajit
authors.initials: S
authors.lastname: Bhattacharya
authors.forename: G R K
authors.initials: GR
authors.lastname: Sarma
mesh_terms: D000368:Aged; D000465:Algorithms; D003198:Computer Simulation; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care; D011379:Prognosis; D012044:Regression Analysis; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012720:Severity of Illness Index; D020521:Stroke / Q000628:therapy*; D000071939:Stroke Rehabilitation / Q000379:methods*; D016896:Treatment Outcome