Knowledge Graph Embedding with Electronic Health Records Data

Abstract: Due to the increasing adoption of electronic health records (EHR), large scale EHRshave become another rich data source for translational clinical research. 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 tocluster 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. Bio: Junwei Lu is an Assistant Professor of Biostatistics, Department of Biostatistics, Harvard T.H. Chan School of Public Health. His research focuses on the intersection of statistical machine learning and clinical studies, revealing scientific associations among the clinical treatment strategies and patient phenotyping, especially focusing on precision medicine leveraging real-world clinical data such as electronic health records data for risk prediction and clinical optimization. Zoom link: https://bit.ly/3tme8ec Passcode: 874260