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.
@article{arxiv.1602.07280,
title = {A Statistical Model for Stroke Outcome Prediction and Treatment Planning},
author = {Abhishek Sengupta and Vaibhav Rajan and Sakyajit Bhattacharya and G R K Sarma},
journal= {arXiv preprint arXiv:1602.07280},
year = {2016}
}