A statistical model for stroke outcome prediction and treatment planning.

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.