  Increased availability of electronic health records (EHR) has enabled
researchers to study various medical questions. Cohort selection for the
hypothesis under investigation is one of the main consideration for EHR
analysis. For uncommon diseases, cohorts extracted from EHRs contain very
limited number of records - hampering the robustness of any analysis. Data
augmentation methods have been successfully applied in other domains to address
this issue mainly using simulated records. In this paper, we present ODVICE, a
data augmentation framework that leverages the medical concept ontology to
systematically augment records using a novel ontologically guided Monte-Carlo
graph spanning algorithm. The tool allows end users to specify a small set of
interactive controls to control the augmentation process. We analyze the
importance of ODVICE by conducting studies on MIMIC-III dataset for two
learning tasks. Our results demonstrate the predictive performance of ODVICE
augmented cohorts, showing ~30% improvement in area under the curve (AUC) over
the non-augmented dataset and other data augmentation strategies.
