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Activity Number: 319
Type: Contributed
Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #309968
Title: Using Machine Learning to Identify Best Treatment Subgroup Characteristics
Author(s): Barry Eggleston*+ and Georgiy Bobashev and Nikhil Garge
Companies: RTI International and RTI International and RTI International
Keywords: Machine Learning ; COMBINE ; Alcohol Research
Abstract:

Because of the predictive strength of Machine Learning methods, researchers are demonstrating their utility to aid clinicians in personalized treatment. Machine Learning has the capability of producing predictive models that maintain predictive accuracy on independent datasets as well as identifying predictor variables that are important in creating an accurate model. In addition, appropriately integrated Machine Learning methodologies enable a researcher to predict best treatment. By harnessing best treatment prediction and important predictor variables identification, a researcher can further characterize patients who have the same best predicted treatment. In this paper we will illustrate the use of Machine Learning methodologies to identify best treatment in a dataset from the COMBINE study for alcohol dependency, and illustrate how these methods can be used to identify common baseline characteristics for each subgroup of subjects who have the same predicted best treatment. Finally, using the subset of subjects from COMBINE that received the predicted best treatment, we will consider the significance of these common baseline characteristics for predicting best treatment.


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