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