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Mining Electronic Health Records using Linked Data.

Odgers DJ, Dumontier M - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Annotator Workflow for Clinical Note annotation.
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f1-2091678: Annotator Workflow for Clinical Note annotation.

Mentions: STRIDE is a central repository for EHR data from the Lucile Packard Children’s Hospital and Stanford Hospital and Clinics. The subset of EHR data that we have used for this system is generated from 18 years of data (1994–2011), 1.8 million patients, 19 million encounters, 35 million coded ICD9 diagnosis and more than 11 million unstructured clinical notes which are a combination of pathology, radiology and transcription reports. The dataset includes both inpatient and outpatient notes that include radiology, pathology, and transcription reports. Figure 1 demonstrates the workflow to annotate clinical notes in STRIDE 14,15,16,17. Additionally, prescription and detailed visit information with structured International Classification of Disease (ICD9) codes and Current Procedural Terminology (CPT) codes are available.


Mining Electronic Health Records using Linked Data.

Odgers DJ, Dumontier M - AMIA Jt Summits Transl Sci Proc (2015)

Annotator Workflow for Clinical Note annotation.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4525267&req=5

f1-2091678: Annotator Workflow for Clinical Note annotation.
Mentions: STRIDE is a central repository for EHR data from the Lucile Packard Children’s Hospital and Stanford Hospital and Clinics. The subset of EHR data that we have used for this system is generated from 18 years of data (1994–2011), 1.8 million patients, 19 million encounters, 35 million coded ICD9 diagnosis and more than 11 million unstructured clinical notes which are a combination of pathology, radiology and transcription reports. The dataset includes both inpatient and outpatient notes that include radiology, pathology, and transcription reports. Figure 1 demonstrates the workflow to annotate clinical notes in STRIDE 14,15,16,17. Additionally, prescription and detailed visit information with structured International Classification of Disease (ICD9) codes and Current Procedural Terminology (CPT) codes are available.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus