text: 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.
  @cite_5: Background Recently there has been an explosion of new data sources about genes, proteins, genetic variations, chemical compounds, diseases and drugs. Integration of these data sources and the identification of patterns that go across them is of critical interest. Initiatives such as Bio2RDF and LODD have tackled the problem of linking biological data and drug data respectively using RDF. Thus far, the inclusion of chemogenomic and systems chemical biology information that crosses the domains of chemistry and biology has been very limited
  @cite_6: Meaningful Use guidelines have pushed the United States Healthcare System to adopt electronic health record systems (EHRs) at an unprecedented rate. Hospitals and medical centers are providing access to clinical data via clinical data warehouses such as i2b2, or Stanford’s STRIDE database. In order to realize the potential of using these data for translational research, clinical data warehouses must be interoperable with standardized health terminologies, biomedical ontologies, and growing networks of Linked Open Data such as Bio2RDF. Applying the principles of Linked Data, we transformed a de-identified version of the STRIDE into a semantic clinical data warehouse containing visits, labs, diagnoses, prescriptions, and annotated clinical notes. We demonstrate the utility of this system though basic cohort selection, phenotypic profiling, and identification of disease genes. This work is significant in that it demonstrates the feasibility of using semantic web technologies to directly exploit existing biomedical ontologies and Linked Open Data.
  @cite_3: Bio2RDF is an open source project to generate and provide Linked Data for the Life Sciences. Here, we report on a third coordinated release of ∼11 billion triples across 30 biomedical databases and datasets, representing a 10 fold increase in the number of triples since Bio2RDF Release 2 (Jan 2013). New clinically relevant datasets have been added. New features in this release include improved data quality, typing of every URI, extended dataset statistics, tighter integration, and a refactored linked data platform. Bio2RDF data is available via REST services, SPARQL endpoints, and downloadable files.
  @cite_8: The Linked Open Data (LOD) community project at the World Wide Web Consortium (W3C) is publishing various open data sets as Resource Description Framework (RDF) on the Web and extending it by setting RDF links between data items from different data sources containing information about genes, proteins, pathways, diseases, and drugs. While this presents a very powerful platform for federated querying and heterogeneous data integration, its true potential can only be realized when combining such information with "real patient" data from electronic health records. In this paper, we report our early experiences in applying Linked Data principles and technologies for representing patient data from electronic health records (EHRs) at Mayo Clinic in RDF. In particular, we demonstrate a proof-of-concept case study leveraging publicly available data from the Linked Open Drug Data cloud to federated querying for type 2 diabetes patients. Our study highlights several challenges and opportunities in using Semantic Web tools and technologies within a healthcare setting for enabling clinical and translational research.
summary: In order to bring the advantages of Semantic Web to the life science community, a number of biomedical KGs have been constructed over the last years, such as Bio2RDF @cite_3 and Chem2Bio2RDF @cite_5 . These datasets make the interconnection and exploration of different biomedical data sources possible. However, there is little patients clinical information within these biomedical KGs. STRIDE2RDF @cite_6 and MCLSS2RDF @cite_8 apply Linked Data Principles to represent patientʼs electronic health records, but the interlinks from clinical data to existing biomedical KGs are still very limited. Hence, none of the existing linked datasets are bridging the gap between clinical and biomedical data.