  Labeling patients in electronic health records with respect to their statuses
of having a disease or condition, i.e. case or control statuses, has
increasingly relied on prediction models using high-dimensional variables
derived from structured and unstructured electronic health record data. A major
hurdle currently is a lack of valid statistical inference methods for the case
probability. In this paper, considering high-dimensional sparse logistic
regression models for prediction, we propose a novel bias-corrected estimator
for the case probability through the development of linearization and variance
enhancement techniques. We establish asymptotic normality of the proposed
estimator for any loading vector in high dimensions. We construct a confidence
interval for the case probability and propose a hypothesis testing procedure
for patient case-control labelling. We demonstrate the proposed method via
extensive simulation studies and application to real-world electronic health
record data.
