  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. In this paper we
design a new model to predict outcome in the short-term, the putative
therapeutic window for several treatments. Our regression-based model has a
parametric form that is designed to address many challenges common in medical
datasets like highly correlated variables and class imbalance. Empirically our
model outperforms the best--known previous models in predicting short--term
outcomes and in inferring the most effective treatments that improve outcome.
