PDD Graph: Bridging Electronic Medical Records and Biomedical Knowledge Graphs via Entity Linking
This work addresses the challenge of integrating and utilizing medical knowledge from EMRs for healthcare professionals, though it is incremental as it builds on existing biomedical knowledge graphs.
The authors tackled the problem of capturing relationships between symptoms, diagnoses, and treatments in electronic medical records by constructing a large heterogeneous graph linking patients, diseases, and drugs (PDD) from MIMIC-III data, automatically linking it to biomedical knowledge graphs like ICD-9 and DrugBank, and making it accessible via a SPARQL endpoint for applications such as treatment recommendations.
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.