Abstract Details
|
Activity Number:
|
248
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Monday, August 4, 2014 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
Section on Statistical Computing
|
|
Abstract #313004
|
|
|
Title:
|
Classification of Clinical Outcomes Using High-Throughput Informatics
|
|
Author(s):
|
Alexander Cambon*+ and Shesh N. Rai
|
|
Companies:
|
University of Louisville and University of Louisville
|
|
Keywords:
|
classification ;
machine learning ;
treatment subset ;
dimension reduction ;
clinical study
|
|
Abstract:
|
Various distance measures for nonparametric and parametric classification methods are identified. These distance measures are then extended for use in methods involving treatment subset prediction in clinical studies involving high throughput data. Simulation results are presented, along with discussion and conclusions and ideas for further extensions and applications.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2014 program
|
2014 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Professional Development program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.