Abstract Details
Activity Number:
|
473
|
Type:
|
Invited
|
Date/Time:
|
Wednesday, August 6, 2014 : 10:30 AM to 12:20 PM
|
Sponsor:
|
ENAR
|
Abstract #310568
|
|
Title:
|
New Developments in Machine Learning for Personalized Medicine
|
Author(s):
|
Michael Kosorok*+
|
Companies:
|
University of North Carolina at Chapel Hill
|
Keywords:
|
Personalized medicine ;
Machine learning ;
Biomarkers ;
Clinical trials ;
Outcome weighted learning ;
Dose finding
|
Abstract:
|
In this talk, we present several new developments in machine learning which were inspired by questions in personalized medicine. The new approaches involve indirect methods for finding biomarkers which can identify subgroups of patients who respond optimally to a specific drug or drug dose. One of the approaches is a form of latent supervised learning and the other is a generalization of outcome weighted learning to continuous dose levels. We illustrate the approaches with simulation studies and discuss writing clinical research protocols that implement the new methods.
|
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.