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Electronic Health Records (EHR) data, a rich source for biomedical research,have been successfully used to gain novel insight into a wide range ofdiseases. Despite its potential, EHR is currently underutilized for discoveryresearch due to it's major limitation in the lack of precise phenotypeinformation. To overcome such difficulties, recent efforts have been devoted todeveloping supervised algorithms to accurately predict phenotypes based onrelatively small training datasets with gold standard labels extracted viachart review. However, supervised methods typically require a sizable trainingset to yield generalizable algorithms especially when the number of candidatefeatures, $p$, is large. In this paper, we propose a semi-supervised (SS) EHRphenotyping method that borrows information from both a small labeled datawhere both the label $Y$ and the feature set $X$ are observed and a much largerunlabeled 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 highdimensional setting. Under a working prior assumption that $S$ is related to$X$ only through $Y$ and allowing it to hold approximately, we propose a prioradaptive semi-supervised (PASS) estimator that adaptively incorporates theprior knowledge by shrinking the estimator towards a direction derived underthe prior. We derive asymptotic theory for the proposed estimator anddemonstrate its superiority over existing estimators via simulation studies.The proposed method is applied to an EHR phenotyping study of rheumatoidarthritis at Partner's Healthcare.

Prior Adaptive Semi-supervised Learning with Application to EHR Phenotyping