Budgeted Learning For Developing Personalized Treatment
There is increased interest in using patient-specific
information to personalize treatment. Personalized treatment
decision rules can be learned using data from standard clinical
trials, but such trials are very costly to run. This paper
explores the use of budgeted learning techniques to design
more efficient clinical trials, by effectively determining which
type of patients to recruit, at each time, throughout the
duration of the trial. We propose a Bayesian bandit model and
discuss the computational challenges and issues pertaining to
this approach. We compare our budgeted learning algorithm,
which approximately minimizes the Bayes risk, using both
simulated data and data modeled after a clinical trial for
treating depressed individuals, with other plausible algorithms.
We show that our budgeted learning algorithm demonstrated
excellent performance across a wide variety of situations.
Citation
K. Deng,
R. Greiner,
S. Murphy.
"Budgeted Learning For Developing Personalized Treatment".
International Conference on Machine Learning and Applications (ICMLA), pp 7-14, December 2014.
| Keywords: |
machine learning, budgeted learning, experimental design, personalized treatment |
| Category: |
In Conference |
| Web Links: |
DOI |
BibTeX
@incollection{Deng+al:ICMLA14,
author = {Kun Deng and Russ Greiner and Susan Murphy},
title = {Budgeted Learning For Developing Personalized Treatment},
Pages = {7-14},
booktitle = {International Conference on Machine Learning and Applications
(ICMLA)},
year = 2014,
}Last Updated: February 11, 2020
Submitted by Sabina P 
