Predictive genomics of cardioembolic stroke

Rachel Badovinac Ramoni, Blanca E. Himes, Michele M. Sale, Karen L. Furie, Marco F. Ramoni

Research output: Contribution to journalArticlepeer-review

Abstract

Cardioembolic stroke is a complex disease resulting from the interaction of numerous factors. Using data from Genes Affecting Stroke Risk and Outcome Study (GASROS), we show that a multivariate predictive model built using Bayesian networks is able to achieve a predictive accuracy of 86% on the fitted values as computed by the area under the receiver operating characteristic curve relative to that of the individual single nucleotide polymorphism with the highest prognostic performance (area under the receiver operating characteristic curve=60%).

Original languageEnglish (US)
Pages (from-to)S67-S70
JournalStroke
Volume40
Issue number3 SUPPL. 1
DOIs
StatePublished - Mar 2009

Keywords

  • Bayesian networks
  • Genetics
  • Ischemic stroke
  • Prediction
  • Risk factors

ASJC Scopus subject areas

  • Clinical Neurology
  • Cardiology and Cardiovascular Medicine
  • Advanced and Specialized Nursing

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