  Risk modeling with EHR data is challenging due to a lack of direct
observations on the disease outcome, and the high dimensionality of the
candidate predictors. In this paper, we develop a surrogate assisted
semi-supervised-learning (SAS) approach to risk modeling with high dimensional
predictors, leveraging a large unlabeled data on candidate predictors and
surrogates of outcome, as well as a small labeled data with annotated outcomes.
The SAS procedure borrows information from surrogates along with candidate
predictors to impute the unobserved outcomes via a sparse working imputation
model with moment conditions to achieve robustness against mis-specification in
the imputation model and a one-step bias correction to enable interval
estimation for the predicted risk. We demonstrate that the SAS procedure
provides valid inference for the predicted risk derived from a high dimensional
working model, even when the underlying risk prediction model is dense and the
risk model is mis-specified. We present an extensive simulation study to
demonstrate the superiority of our SSL approach compared to existing supervised
methods. We apply the method to derive genetic risk prediction of type-2
diabetes mellitus using a EHR biobank cohort.
