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Electronic medical records contain multi-format electronic medical data thatconsist of an abundance of medical knowledge. Facing with patient's symptoms,experienced caregivers make right medical decisions based on their professionalknowledge that accurately grasps relationships between symptoms, diagnosis andcorresponding treatments. In this paper, we aim to capture these relationshipsby constructing a large and high-quality heterogenous graph linking patients,diseases, and drugs (PDD) in EMRs. Specifically, we propose a novel frameworkto extract important medical entities from MIMIC-III (Medical Information Martfor Intensive Care III) and automatically link them with the existingbiomedical knowledge graphs, including ICD-9 ontology and DrugBank. The PDDgraph presented in this paper is accessible on the Web via the SPARQL endpoint,and provides a pathway for medical discovery and applications, such aseffective treatment recommendations.

PDD Graph: Bridging Electronic Medical Records and Biomedical Knowledge Graphs via Entity Linking