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Abstract Details

Activity Number: 6
Type: Invited
Date/Time: Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
Sponsor: Social Statistics Section
Abstract - #303633
Title: Unsupervised Learning to Describe Heterogeneity in Psychiatric Conditions
Author(s): Thaddeus Tarpey*+ and Eva Petkova
Companies: Wright State University and NYU School of Medicine
Address: Department of Mathematics & Statistics, Dayton, OH, 45435,
Keywords: clustering ; functional data analysis ; growth mixture models
Abstract:

Understanding heterogeneity in phenotypical characteristics, symptoms manifestations and response to treatment of subjects with psychiatric illnesses continues to be a challenge in mental health research. An important goal in medical research is to identify groups of subjects characterized with a particular trait or quality and to distinguish them from other subjects in a clinically relevant way. In this talk, we explore various unsupervised learning approaches to partition and describe heterogeneity of psychiatric conditions.


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