  A treatment regime is a rule that assigns a treatment to patients based on
their covariate information. Recently, estimation of the optimal treatment
regime that yields the greatest overall expected clinical outcome of interest
has attracted a lot of attention. In this work, we consider estimation of the
optimal treatment regime with electronic medical record data under a
semi-supervised setting. Here, data consist of two parts: a set of `labeled'
patients for whom we have the covariate, treatment and outcome information, and
a much larger set of `unlabeled' patients for whom we only have the covariate
information. We proposes an imputation-based semi-supervised method, utilizing
`unlabeled' individuals to obtain a more efficient estimator of the optimal
treatment regime. The asymptotic properties of the proposed estimators and
their associated inference procedure are provided. Simulation studies are
conducted to assess the empirical performance of the proposed method and to
compare with a fully supervised method using only the labeled data. An
application to an electronic medical record data set on the treatment of
hypotensive episodes during intensive care unit (ICU) stays is also given for
further illustration.

Methodology;Machine Learning