ArXiv Preprint
Dynamic treatment regimes assign personalized treatments to patients
sequentially over time based on their baseline information and time-varying
covariates. In mobile health applications, these covariates are typically
collected at different frequencies over a long time horizon. In this paper, we
propose a deep spectral Q-learning algorithm, which integrates principal
component analysis (PCA) with deep Q-learning to handle the mixed frequency
data. In theory, we prove that the mean return under the estimated optimal
policy converges to that under the optimal one and establish its rate of
convergence. The usefulness of our proposal is further illustrated via
simulations and an application to a diabetes dataset.
Yuhe Gao, Chengchun Shi, Rui Song
2023-01-03
