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.
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.
@cite_5 @cite_6 @cite_3 @cite_8
2778697372 2411062807 2064670228 1807996340
A significant portion of biomedical literature is represented in a manner that makes it difficult for consumers to find or aggregate content through a computational query. One approach to facilitate reuse of the scientific literature is to structure this information as linked data using standardized web technologies. In this paper we present the second version of Biotea, a semantic, linked data version of the open-access subset of PubMed Central that has been enhanced with specialized annotation pipelines that uses existing infrastructure from the National Center for Biomedical Ontology. We expose our models, services, software and datasets. Our infrastructure enables manual and semi-automatic annotation, resulting data are represented as RDF-based linked data and can be readily queried using the SPARQL query language. We illustrate the utility of our system with several use cases. Our datasets, methods and techniques are available at http: biotea.github.io. 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. 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. The World Health Organization is beginning to use Semantic Web technologies in the development of the 11th revision of the International Classification of Diseases (ICD-11). Health officials use ICD in all United Nations member countries to compile basic health statistics, to monitor health-related spending, and to inform policy makers. While previous revisions of ICD encoded minimal information about a disease, and were mainly published as books and tabulation lists, the creators of ICD-11 envision that it will become a multipurpose and coherent classification ready for electronic health records. Most important, they plan to have ICD-11 applied for a much broader variety of uses than previous revisions. The new requirements entail significant changes in the way we represent disease information, as well as in the technologies and processes that we use to acquire the new content. In this paper, we describe the previous processes and technologies used for developing ICD. We then describe the requirements for the new development process and present the SemanticWeb technologies that we use for ICD-11. We outline the experiences of the domain experts using the software system that we implemented using Semantic Web technologies. We then discuss the benefits and challenges in following this approach and conclude with lessons learned from this experience.