AAAI Publications, Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence

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An Interpretable Stroke Prediction Model using Rules and Bayesian Analysis
Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, David Madigan

Last modified: 2013-06-29

Abstract


We aim to produce predictive models that are not only accurate, but are also interpretable to human experts. We introduce a generative model called the Bayesian List Machine for fitting decision lists, a type of interpretable classifier, to data. We use the model to predict stroke in atrial fibrillation patients, and produce predictive models that are simple enough to be understood by patients yet significantly outperform the medical scoring systems currently in use.

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