  The effective analysis of high-dimensional Electronic Health Record (EHR)
data, with substantial potential for healthcare research, presents notable
methodological challenges. Employing predictive modeling guided by a knowledge
graph (KG), which enables efficient feature selection, can enhance both
statistical efficiency and interpretability. While various methods have emerged
for constructing KGs, existing techniques often lack statistical certainty
concerning the presence of links between entities, especially in scenarios
where the utilization of patient-level EHR data is limited due to privacy
concerns. In this paper, we propose the first inferential framework for
deriving a sparse KG with statistical guarantee based on the dynamic log-linear
topic model proposed by \cite{arora2016latent}. Within this model, the KG
embeddings are estimated by performing singular value decomposition on the
empirical pointwise mutual information matrix, offering a scalable solution. We
then establish entrywise asymptotic normality for the KG low-rank estimator,
enabling the recovery of sparse graph edges with controlled type I error. Our
work uniquely addresses the under-explored domain of statistical inference
about non-linear statistics under the low-rank temporal dependent models, a
critical gap in existing research. We validate our approach through extensive
simulation studies and then apply the method to real-world EHR data in
constructing clinical KGs and generating clinical feature embeddings.
