Proceedings of the 1st Machine Learning for Healthcare Conference, PMLR 56:282-300, 2016.
Abstract
We study the problem of estimating the continuous response over time of actions from observational time series—a retrospective dataset where the policy by which the data are generated are unknown to the learner. We develop a novel method based on Bayesian nonparametrics (BNP) that can flexibly model functional data and provide posterior inference over the treatment response curves both at the individual and population level. On a challenging dataset containing time series from patients admitted to a hospital, we estimate treatment responses for 8 different treatments used in managing blood pressure and kidney function and show that the resulting fits are more accurate than alternative approaches. Accurate methods for obtaining ITRs from observational data can dramatically accelerate the pace at which personalized treatment plans become possible.
@InProceedings{pmlr-v56-Xu16,
title = {A Non-parametric Bayesian Approach for Estimating Treatment-Response Curves from Sparse Time Series},
author = {Yanbo Xu and Yanxun Xu and Suchi Saria},
booktitle = {Proceedings of the 1st Machine Learning for Healthcare Conference},
pages = {282--300},
year = {2016},
editor = {Finale Doshi-Velez and Jim Fackler and David Kale and Byron Wallace and Jenna Weins},
volume = {56},
series = {Proceedings of Machine Learning Research},
address = {Northeastern University, Boston, MA, USA},
month = {18--19 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v56/Xu16.pdf},
url = {http://proceedings.mlr.press/v56/Xu16.html},
abstract = {We study the problem of estimating the continuous response over time of actions from observational time series—a retrospective dataset where the policy by which the data are generated are unknown to the learner. We develop a novel method based on Bayesian nonparametrics (BNP) that can flexibly model functional data and provide posterior inference over the treatment response curves both at the individual and population level. On a challenging dataset containing time series from patients admitted to a hospital, we estimate treatment responses for 8 different treatments used in managing blood pressure and kidney function and show that the resulting fits are more accurate than alternative approaches. Accurate methods for obtaining ITRs from observational data can dramatically accelerate the pace at which personalized treatment plans become possible.}
}
%0 Conference Paper
%T A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves
%A Yanbo Xu
%A Yanxun Xu
%A Suchi Saria
%B Proceedings of the 1st Machine Learning for Healthcare Conference
%C Proceedings of Machine Learning Research
%D 2016
%E Finale Doshi-Velez
%E Jim Fackler
%E David Kale
%E Byron Wallace
%E Jenna Weins
%F pmlr-v56-Xu16
%I PMLR
%J Proceedings of Machine Learning Research
%P 282--300
%U http://proceedings.mlr.press
%V 56
%W PMLR
%X We study the problem of estimating the continuous response over time of actions from observational time series—a retrospective dataset where the policy by which the data are generated are unknown to the learner. We develop a novel method based on Bayesian nonparametrics (BNP) that can flexibly model functional data and provide posterior inference over the treatment response curves both at the individual and population level. On a challenging dataset containing time series from patients admitted to a hospital, we estimate treatment responses for 8 different treatments used in managing blood pressure and kidney function and show that the resulting fits are more accurate than alternative approaches. Accurate methods for obtaining ITRs from observational data can dramatically accelerate the pace at which personalized treatment plans become possible.
TY - CPAPER
TI - A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves
AU - Yanbo Xu
AU - Yanxun Xu
AU - Suchi Saria
BT - Proceedings of the 1st Machine Learning for Healthcare Conference
PY - 2016/12/10
DA - 2016/12/10
ED - Finale Doshi-Velez
ED - Jim Fackler
ED - David Kale
ED - Byron Wallace
ED - Jenna Weins
ID - pmlr-v56-Xu16
PB - PMLR
SP - 282
DP - PMLR
EP - 300
L1 - http://proceedings.mlr.press/v56/Xu16.pdf
UR - http://proceedings.mlr.press/v56/Xu16.html
AB - We study the problem of estimating the continuous response over time of actions from observational time series—a retrospective dataset where the policy by which the data are generated are unknown to the learner. We develop a novel method based on Bayesian nonparametrics (BNP) that can flexibly model functional data and provide posterior inference over the treatment response curves both at the individual and population level. On a challenging dataset containing time series from patients admitted to a hospital, we estimate treatment responses for 8 different treatments used in managing blood pressure and kidney function and show that the resulting fits are more accurate than alternative approaches. Accurate methods for obtaining ITRs from observational data can dramatically accelerate the pace at which personalized treatment plans become possible.
ER -
Xu, Y., Xu, Y. & Saria, S.. (2016). A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves. Proceedings of the 1st Machine Learning for Healthcare Conference, in PMLR 56:282-300
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