A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves
Proceedings of the 1st Machine Learning for Healthcare Conference, PMLR 56:282-300, 2016.
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.