  Due to the increasing adoption of electronic health records (EHR), large
scale EHRs have become another rich data source for translational clinical
research. Despite its potential, deriving generalizable knowledge from EHR data
remains challenging. First, EHR data are generated as part of clinical care
with data elements too detailed and fragmented for research. Despite recent
progress in mapping EHR data to common ontology with hierarchical structures,
much development is still needed to enable automatic grouping of local EHR
codes to meaningful clinical concepts at a large scale. Second, the total
number of unique EHR features is large, imposing methodological challenges to
derive reproducible knowledge graph, especially when interest lies in
conditional dependency structure. Third, the detailed EHR data on a very large
patient cohort imposes additional computational challenge to deriving a
knowledge network. To overcome these challenges, we propose to infer the
conditional dependency structure among EHR features via a latent graphical
block model (LGBM). The LGBM has a two layer structure with the first providing
semantic embedding vector (SEV) representation for the EHR features and the
second overlaying a graphical block model on the latent SEVs. The block
structures on the graphical model also allows us to cluster synonymous features
in EHR. We propose to learn the LGBM efficiently, in both statistical and
computational sense, based on the empirical point mutual information matrix. We
establish the statistical rates of the proposed estimators and show the perfect
recovery of the block structure. Numerical results from simulation studies and
real EHR data analyses suggest that the proposed LGBM estimator performs well
in finite sample.
