  Electronic Health Records (EHR) data, a rich source for biomedical research,
have been successfully used to gain novel insight into a wide range of
diseases. Despite its potential, EHR is currently underutilized for discovery
research due to it's major limitation in the lack of precise phenotype
information. To overcome such difficulties, recent efforts have been devoted to
developing supervised algorithms to accurately predict phenotypes based on
relatively small training datasets with gold standard labels extracted via
chart review. However, supervised methods typically require a sizable training
set to yield generalizable algorithms especially when the number of candidate
features, $p$, is large. In this paper, we propose a semi-supervised (SS) EHR
phenotyping method that borrows information from both a small labeled data
where both the label $Y$ and the feature set $X$ are observed and a much larger
unlabeled data with observations on $X$ only as well as a surrogate variable
$S$ that is predictive of $Y$ and available for all patients, under a high
dimensional setting. Under a working prior assumption that $S$ is related to
$X$ only through $Y$ and allowing it to hold approximately, we propose a prior
adaptive semi-supervised (PASS) estimator that adaptively incorporates the
prior knowledge by shrinking the estimator towards a direction derived under
the prior. We derive asymptotic theory for the proposed estimator and
demonstrate its superiority over existing estimators via simulation studies.
The proposed method is applied to an EHR phenotyping study of rheumatoid
arthritis at Partner's Healthcare.
