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SHARPn Data Normalization. November 18, 2013. Data-driven Healthcare. Big Data . Analytics. Domain Pragmatics. Research. Practice. Experts. Knowledge. A framework for clinical data reuse. Production Systems. Production Databases. Replicate. Replicate. Query. Data Analytics - PowerPoint PPT Presentation
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SHARPn Data Normalization
November 18, 2013
Data-driven Healthcare
Big Data
Knowledge
Research Pr
actic
e
Analytics
Domain Pragmatics
Experts
A framework for clinical data reuse
Replicate Replicate
Query
Production Systems
Production Databases
Enterprise Repository/Data Warehouse
Workgroup Datamarts
Query Query
Workflow or goal specific
Data AnalyticsNLP and Data Normalization
SHARPn Data Normalization
Goals– To conduct the science for realizing semantic
interoperability and integration of diverse data sources
– To develop tools and resources enabling the generation of normalized EMR data for portable and scalable secondary uses
Data Normalization
Information Models
Target Value Sets
Raw EMR Data
Tooling
Normalized EMR Data
Normalization Targets
Normalization Process
Normalization Targets
Clinical Element Models– Based on Intermountain Healthcare/GE
Healthcare’s detailed clinical modelsTerminology/value sets associated with
the models– Using standards where possible
Normalization Process
Configuration of Model (Syntactic) and Terminology (Semantic) Mapping
UIMA Pipeline to transform raw EMR data to normalized EMR data based on mappings
Four Subprojects
Clinical Information Modeling Value Sets Management End-to-End Pipeline Normalized Data Representation and
Store
Secondary Use Clinical Element Models
GenericStatement GenericComponent
Core CEMs
SecondaryUse CEMs
Links
AdministrativeGender, …Severity, Status
Embracing the fact that data may not be able to be normalized and enabling bottom-up and top-down
http://www.clinicalelement.com
Status of Secondary Use CEMs
Model specification is finalCEM Browser is in productionManuscript is in preparation
Future:Secondary Use CEMs and CEM Browser will be maintained through Clinical Information Modeling Initiative (CIMI)
SecondaryUseNotedDrug – Output (1/2)
SecondaryUseNotedDrug – Output (2/2)
NLP in data normalization
A large amount of clinical information is in clinical narratives, NLP is a critical component in data normalization
cTAKES has been wrapped into the data normalization pipeline to normalize data in clinical narratives
End-to-end DN framework
Data Normalization version 2
http://sourceforge.net/p/sharpn/datan/code/HEAD/tree/
DN activities after SHARPn (1) – Clinical Information Model Initiatives
DN activities after SHARPn (2) – Open Health Natural Language Processing
(OHNLP)
Use of the Data Normalization information model as the base to define a Common Type System to capture basic clinical information models
Use of the Data Normalization pipeline to improve interoperability of various clinical information models
DN activities after SHARPn (3) – Clinical decision support and phenotyping
The use of NLP and Big Data for Late Binding Data Normalization
Practical implementation of Late Binding Data Normalization and Drools for real-time clinical decision support