CDISC Presentation
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This document discusses how the Semantic Web can be used in healthcare to link health data and bridge the "patient gap" between different systems and data formats. It proposes that clinical data standards like CDISC can help drive this by representing their terms and codes in ontologies and making clinical data from electronic medical records queryable through languages like SPARQL. By putting EMRs on the web and making their data accessible via the Semantic Web, this could create a single linked health data network usable by patients, doctors, researchers and others.
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OBDA is a popular paradigm, developed since the mid 2000s, to query
various types of data sources
using a common vocabulary familiar to the end-users. In a nutshell
OBDA separates the user
from the data sources (relational databases, CVS files, etc.) by means
of an ontology, which is a common terminology that provides the user with a
convenient query vocabulary, hides the structure of the data sources,
and can enrich incomplete data with background knowledge. About a
dozen OBDA systems have been implemented in both academia and
industry.
In this tutorial we will give an overview of OBDA, and our system -ontop-
which is currently being used in the context of the European project
Optique. We will discuss how to use -ontop- for data integration,
in particular concentrating on:
– How to create an ontology (common vocabulary) for a life science domain.
– How to map available data sources to this ontology.
– How to query the database using the terms in the ontology.
– How to check consistency of the data sources w.r.t. the ontology
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ALA 2010 -- Jabin White
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This presentation demonstrates the functionality provided by the Logical Model Designer (LMD) and Snow Owl tools, which enables terminology to be bound to the Singapore Logical Information Model.
Abstract:
A critical enabler in the journey towards semantic interoperability in Singapore is the Singapore "˜Logical Information Model' (LIM). The LIM is a model of the healthcare information shared within Singapore, and is defined as a set of reusable "˜archetypes' for each clinical concept (e.g. Problem/Diagnosis, Pharmacy Order). These archetypes are then constrained and composed into "˜templates' to support specific use cases.
The Singapore LIM harmonises the semantics of the information structures with the terminology, using multiple types of terminology bindings, including semantic, value domain and constraint bindings. Value domain bindings are defined to both national "˜reference terminology' (used for querying nationally-collated data), as well as to a variety of "˜interface terminologies' used within local clinical systems (required to enforce conformance-compliance rules over message specifications generated from the LIM). To support the diversity of pre-coordination captured in local interface terms, "˜design patterns' are included in the LIM, based on the SNOMED CT concept model. These design patterns represent a logical model of meaning for a specific concept, and allow more than one split between the information model and the terminology model to be represented in a semantically-consistent manner.
This presentation will demonstrate the "˜Logical Model Designer' (LMD) - an Eclipse-based tool that is being used to maintain Singapore's Logical Information Model. A number of features of the LMD tooling will be demonstrated, with a specific focus on how the information structure is bound to the terminology via an interface to the Snow Owl platform. Value Domains are defined as reference sets within Snow Owl and then linked to the information structures defined in the LMD.
Please see our website http://b2i.sg for further information.
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https://www.big-data-europe.eu/the-final-big-data-europe-workshop/
2011-12-02 Open PHACTS at STM Innovation
A 5-minute flash session (timed 15 secs/slide) presented by Richard Kidd at the STM Innovation Seminar 2011.
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This document describes a case study on using open linked data in bioinformatics. It provides an introduction to semantic web technologies like RDF, URIs, and SPARQL. It then discusses the Bio2RDF project, which publishes biological data from sources like KEGG and OMIM in RDF format. A use case scenario is presented where SPARQL queries are used to find diseases associated with genes in a given pathway by querying multiple Bio2RDF datasets in a federated manner. The results demonstrate benefits over traditional methods. Future work aims to develop user interfaces and monitoring systems for querying and tracking updates across datasets.
2015-02-10 The Open PHACTS Discovery Platform: Semantic Data Integration for ...
The Open PHACTS Discovery Platform integrates multiple biomedical data resources into a single open access point using semantic web technology. It is guided by business questions from pharmaceutical companies to integrate data from sources like ChEMBL, DrugBank, UniProt, and more. The platform is run as a public-private partnership through 2021 to support drug discovery.
2004 10-19 rudi vdv
The document discusses the design principles and challenges of clinical information systems at AZ-VUB hospital in Belgium. It outlines the hospital's IT strategy of using a multi-layered, component-based architecture across the entire hospital. Key challenges discussed include developing clinical decision support systems, managing workflows, and addressing issues around data storage and mobility.
Dataset Catalogs as a Foundation for FAIR* Data
BioPharma and the broader research community is faced with the challenge of simply finding the appropriate internal and external datasets for downstream analytics, knowledge-generation and collaboration. With datasets as the core asset, we wanted to promote both human and machine exploitability, using web-centric data cataloguing principles as described in the W3C Data on the Web Best Practices. To do so, we adopted DCAT (Data CATalog Vocabulary) and VoID (Vocabulary of Interlinked Datasets) for both RDF and non-RDF datasets at summary, version and distribution levels. Further, we’ve described datasets using a limited set of well-vetted public vocabularies, focused on cross-omics analytes and clinical features of the catalogued datasets.
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- 4. A Linked Patient CodingSystem Code observation observationValue Doe personName familyName givenName middleName about Patient type Identifiers and Time not shown URI: http://www.facilityx.com/cdrs/112123449 LN 30949-2 CodingSystem Code 005 NIP John Fitzgerald
- 7. Enter the Ontologies! Name NDC: 00074337701 Desoxyn 5MG Tablet Name SameAs Ingredient Medication SameAs RxNorm Stanford Drug Ontology Hoot72 Patient Graph * Dotted: composite of links to save space ** w3c HCLS Example RxNorm:6816 Methamphetamine Methamphetamine StanDrug: C0025611 Name Obese May Treat Patient Joe NDC: 00074337701
- 9. Ontologies are Standing by URIs for CDISC Terms?
- 16. Many Users, Contributors One Semantic Web for Health-Care Linked Health Data Patient Doctor Manager Researcher Insurance Informatics
Editor's Notes
- There are green field demos already
- vague generic “go for you” terms page on baltimore becomes linkable data about baltimore web didn’t make hyperlinks or protocols or page layout the power, the scale was link anywhere
- Nodes and Literals ... Codes would break down Detailed discussion of semi-structured (not going to get into this aspect) Observable ( OBX|4|CE|30949-2^Vaccination adverse event outcome^LN|1|005^required hospitalization^NIP|) NIP= National Immunization Program within the Center for Disease Control
- One standard code is 30949-2. For the astute: better if code became URI.
- Beyond an isolated set of patients
- From: HCLS == the w3c Health Care and Life Sciences Interest Group Patient Data is secure - “intranet” LINK OUT Typical Report: chase type (Ontology) in a world of (EMR) particulars. Stanford Drug Ontology gives compounds that treat conditions. RxNorm relates compounds to branded drugs. Hoot72 Clinical Data has branded drugs. RxNorm not yet an ontology but has web api so can represent it as a SPARQL end point Simplied to fit. Ingredient = consists of to ingredient to brand name etc.
- Middleware - format gets out of the way. IT gets out of the reformatting business.
- Growing in number ... Billions of triples, ready to be leveraged, all these URIs. Gen purpose (demographics) and PubMed, Drug Bank, GeneID, Diseasome Arrows representing linking out to another conceptual scheme CDASH ODM (machine readable) CDISC SDTM and other terminology goes through an extensive process of definition, development, and review before it is declared ready for release. Terminology that has completed this process is tagged as "Production," and now includes some 50 SDTM codelists with about 2,200 terms covering demographics, interventions, findings, events, trial design, units, frequency, and ECG terminology. This terminology is maintained and distributed as part of NCI Thesaurus
- CDC Example. We will have standard and local ontologies, standard and local queries there may be several different ways to express the same concept. Human users may be able to recognise that these are essentially the same, but the rules for doing so must be made explicit to be usable by computer. -- Why is Terminology hard?, Alan Rector
- ME to learn of CDISC work. See how to leverage all the work. ... Here at the Drug Information Association (DIA), you can see a “live” implementation of the interoperability that is possible between Electronic Health Record (EHR) systems and Electronic Data Capture (EDC) systems used for clinical research, which leverages the Integrating the Healthcare Enterprise’s (IHE) Retrieve Form for Data Capture (RFD) integration profile along with CDISC’s ODM and CDASH standards Contrast to RDIF XFORMs.
- Get Real The Integration Control Number (ICN) - ASTM e1714-95 standard for a universal health identifier. Like the efforts in the showcase to interop EMRs
- MUMPS ( M assachusetts General Hospital U tility M ulti- P rogramming S ystem) EMR NOT LEFT OUT OF THE PICTURE, not just a “old” aside.
- Very early on this.
- The big picture ... Concept and Concrete, Users and Contributors in one web Trial Recruitment, Drug Safety, Outcomes research
- More than the two ways here