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

Patient-drug-disease (PDD) Graph dataset, utilising Electronic medical records (EMRS) and biomedical Knowledge graphs. The novel framework to construct the PDD graph is described in the associated publication.

PDD is an RDF graph consisting of PDD facts, where a PDD fact is represented by an RDF triple to indicate that a patient takes a drug or a patient is diagnosed with a disease. For instance, (pdd:274671, pdd:diagnosed, sepsis)

Data files are in .nt N-Triple format, a line-based syntax for an RDF graph. These can be accessed via openly-available text edit software.

diagnose_icd_information.nt - contains RDF triples mapping patients to diagnoses. For example:
(pdd:18740, pdd:diagnosed, icd99592),
where pdd:18740 is a patient entity, and icd99592 is the ICD-9 code of sepsis.

drug_patients.nt- contains RDF triples mapping patients to drugs. For example:
(pdd:18740, pdd:prescribed, aspirin),
where pdd:18740 is a patient entity, and aspirin is the drug's name.

Background:
Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge. Faced with patients' symptoms, experienced caregivers make the right medical decisions based on their professional knowledge, which accurately grasps relationships between symptoms, diagnoses and corresponding treatments. In the associated 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 as well as in .nt format in this repository, and provides a pathway for medical discovery and applications, such as effective treatment recommendations.

De-identification
It is necessary to mention that MIMIC-III contains clinical information of patients. Although the protected health information was de-identifed, researchers who seek to use more clinical data should complete an on-line training course and then apply for the permission to download the complete MIMIC-III dataset: https://mimic.physionet.org/