Title: PDD Graph: Bridging Electronic Medical Records and Biomedical Knowledge Graphs via Entity Linking,
Abstract: Electronic medical records contain multi-format electronic medical data that
consist of an abundance of medical knowledge. Facing with patient's symptoms,
experienced caregivers make right medical decisions based on their professional
knowledge that accurately grasps relationships between symptoms, diagnosis and
corresponding treatments. In this paper, we aim to capture these relationships
by constructing a large and high-quality heterogenous graph linking patients,
diseases, and drugs (PDD) in EMRs. Specifically, we propose a novel framework
to extract important medical entities from MIMIC-III (Medical Information Mart
for Intensive Care III) and automatically link them with the existing
biomedical knowledge graphs, including ICD-9 ontology and DrugBank. The PDD
graph presented in this paper is accessible on the Web via the SPARQL endpoint,
and provides a pathway for medical discovery and applications, such as
effective treatment recommendations.