Use of Bayesian Networks to Classify Complex Pathologies

John Eberhardt of DecisionQ Corp will be presenting a tutorial entitled Use of Bayesian Networks to Classify Complex Pathologies at NIH tomorrow. If you’re in the DC area, it might be worth attending. Here are the details:

DATE & TIME: Thursday, November 10, 2005, 3:00 to 4:30 p.m.
LOCATION: NIH Clinical Center (Building 10), Medical Board Room (Room 2C116)
SUITABLITY: Anyone interested in the subject matter.
NIH CONTACT: Jim DeLeo, 301-496-3848, jdeleo@nih.gov
BCIG WEB SITE: www.nih-bcig.org
NIH VISITOR INFORMATION: http://www.nih.gov/about/visitor/

DESCRIPTION: With the proliferation of clinical markers, it is becoming increasingly important to provide clinicians with an information framework for the interpretation of complex pathologic findings. Bayesian Networks provide one such framework in a graphical representation that still produces a quantitative metric as output. These networks allow for the organization and analysis of large numbers of variables. Bayesian Networks also have certain distinguishing characteristics compared to other information frameworks, such as transparency and the ability to render predictions with only partial information. John will discuss the utility and theory of Bayesian Networks and will also present two examples in renal disease and breast cancer using actual lab data.

INSTRUCTOR: John has spent his career in analysis. Starting as an analyst in Morgan Stanley’s investment banking program, he proceeded into a career in Morgan Stanley’s venture capital fund, focusing on the analysis and selection of investments in technology. John left Morgan Stanley four years ago to become one of the founders of DecisionQ Corporation, a data mining and predictive analysis software company focused on the development of tools for simplifying the analysis of complex problems. John has spoken previously at conferences on data mining and entrepreneurship, and has published papers on the use of data mining tools for improving patient outcomes in healthcare.

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