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arXiv · 1602.07280

A Statistical Model for Stroke Outcome Prediction and Treatment Planning

Abhishek Sengupta·Vaibhav Rajan·Sakyajit Bhattacharya·G R K Sarma
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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. 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.

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Abhishek Sengupta, Vaibhav Rajan, Sakyajit Bhattacharya, G R K Sarma. 2016-02-22. A Statistical Model for Stroke Outcome Prediction and Treatment Planning. https://arxiv.org/abs/1602.07280

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