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This is the presentation of the paper presented on the early career symposium at the International Conference on Biomedical Ontology. http://www.unbsj.ca/sase/csas/data/ws/icbo2013/papers/ec/icbo2013_submission_56.pdf
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Unrestricted © Siemens AG 2013. All rights reserved
An OGMS-basedModel for Clinical Information
ICBO Early Career Symposium / July 2013
Heiner Oberkampf1,2, Sonja Zillner1, Bernhard Bauer2 and Matthias Hammon3
1 Siemens AG, Corporate Technology, Munich, Germany2 Software Methodologies for Distributed Systems, University of Augsburg, Germany3 Department of Radiology, University Hospital Erlangen, Germany
Unrestricted © Siemens AG 2013. All rights reservedPage 2 July 2013 Corporate Technology
Agenda
Clinical Data and Problem Description
The Model for Clinical Information
Storage and Queries
Related Work
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Clinical Data
Unstructured, heterogeneous and complex.
• Data is mostly unstructured
• Data is not integrated
• Different data needs to be combined for clinical decisions
• Raw data needs to be interpreted to fasten clinical decisions (e.g. normal, abnormal, pathological …)
• Clinicians do not use the full information contained in existing patient data
Problems
Structured• Diagnosis• Procedures• Lab values• Demographics• Medication• Provider
Unstructured• Reports• Images• Omics data
Data
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Patient Data
The development of our model is based on two datasets.
Project: Data Intelligence for Clinical Solutions(6 manually selected melanoma patients)• Demographics• diagnoses (ICD-10 codes)• lab values (LOINC codes)• procedures (OPS codes)• drug administration (ATC codes)• radiology reports• pathology reports• all data timestamped
Project: Theseus MEDICO (382 patients)• 2700 Radiological Reports• Image Annotations
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Semantic Data Integration
The Hospital in Erlangen integrates their data into an i2b2 data warehouse.
Clinical Information
Systems
Model for Clinical
Information
i2b2
Data-Warehouse
• Raw data• Vendor specific
• Administration centric• BI applications (e.g. Oracle Health Data Model)
• Structured data is mapped to standardized terminologies (ICD, LOINC, OPS…)• Identification of patient cohorts
• Patient centric and granular data representation• Annotation of unstructured data• Use of medical ontologies and reasoning mechanisms
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Questions to Answer
In perspective we want to answer the following questions using Model of Clinical Information (MCI).
Normal and abnormal structures• What findings about abnormalities do we have from
the latest examination?
Longitudinal data access• How does this finding evolved from last
examination?
Disease centric data access• Are there any cancer findings?
Organ centric data access• What information do we have regarding the liver
functioning?
Decision support• What are likely diseases?• What examinations should be provided next?
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Semantic Model for Clinical Information
Agenda
Clinical Data and Problem Description
The Model for Clinical Information
Storage and Queries
Related Work
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Technical Perspective
Technical and Clinical Perspective
Clinical Perspective
• Provide a holistic view on the patient• Make the change of the patient‘s health
status transparent• Point to missing information about
symptoms and signs.• Allow efficient and integrated access to
patient data• Provide a basis for clinical decision support
systems
• MCI integrates and structures clinical data and provides…• core concepts of clinical information• meta-information• interpretations
• Clinical data is expressed using MCI in combination with established ontologies like SNOMED, RadLex, LOINC, ICD …
• MCI is based on established upper level ontologies: BFO, OGMS, IAO …
• Use of medical knowledge contained in ontologies through reasoning
• Use of Linked Open Data: LinkedCT, SIDER, DrugBank, …
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Big Picture
Integrating different data and knowledge resources.
Integrated Data Access
Clinical Information Systems
PACS, reports, treatment plans,
lab values, administrative
data …
Data Repositories(factual knowledge)LinkedCT, DrugBank, SIDER, DBpedia, …
Medical Ontologies (formalized knowledge)
SNOMED CT, ICD, LOINC, ATC, RadLex, DOID, FMA,
OPS, SYMP, …
Model for Clinical Information (MCI)
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Ontologies from the OBO Foundry
MCI is based on established upper ontologies
Basic Formal Ontology (BFO1.1)Completely domain independent
Ontology for General Medical Science (OGMS)basic clinical concepts like, e.g., diagnosis, clinical finding,pathological anatomical structure …
Ontology of Medically Related Social Entities (OMRSE)Patient role, healthcare provider role, gender role …
Information Artifact Ontology (IAO)document, report, figure ...
Relations Ontology (RO)has_part, has_participant, located_in …
Units Ontology (UO)Subset: units for length, mass and time.
Clinical Narrative Temporal Relation Ontology (CNTRO)(not from OBO Foundry)Subset: ObjectProperties before, after, overlap …
Total MCI
Classes 358 62
ObjectProperties 82 20
DatatypeProperties 19 14
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Added Classes (I)
Extensions under ‘obo:data item‘.
mci:patientposition
mci:radiologicalexamination
_:re01 _:pp01
radlex:standingposition
mci:has examination modality
_:ce01
obo:clinicalencounter icd10:C79_8
obo:has specified output
mci:maindiagnosis
_:md01
Examination modality
Diagnosis
rdf:type
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Added Classes (II)
Extensions under ‘obo:clinical finding‘ – interpretations of clinical findings.
mci:approximately
obo:length measurement
datum
0.8^^xsd:decimal
obo:has measurement unit label
obo:imagefinding radlex:lymph
node of thorax
obo:is about
mci:anatomical structure
obo:has measurement value
_:if01 _:as01
_:lmd01uo:centimeter
rdf:type…thoracic lymph node of ca. 0.8 cm.
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Added Properties
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mci:anatomicalstructure
Example Representationof a Radiological Image Finding
Spleen at hilum with diameter 10 x 4 cm
mci:precisely
4^^xsd:decimal
obo:has measurement unit label
obo:imagefinding radlex:spleen
obo:is about
mci:anatomical structure
obo:has measurement value
_:if01 _:as01
uo:centimeter
10^^xsd:decimal
_:lmd02obo:has measurement value
obo:length measurement
datum
_:lmd01
radlex:hilumof spleen
_:as02
PATO:diameter
_:q02
obo:is quality measurement of_:q01
mci:locationspecification
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Semantic Model for Clinical Information
Agenda
Clinical Data and Problem Description
The Model for Clinical Information
Storage and Queries
Related Work
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Jena Fuseki
Triple Store
Datasets and Named Graphs are used to separate triples.
/mci
/radlex
/patient23000PX1LH
/ops
/icd10
/atc
1) We transformed the the OPS hierarchy from in XML to RDF using XSLT.2) We transformed ATC from Excel to RDF
1
2
Named Graphs for clinical encountersNamed Graphs for clinical encounters…
/patient23000JJ43N
Named Graphs for clinical encountersNamed Graphs for clinical encounters…
……
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Querying the Model
All findings about the gastrointestinal tract.
obo:imagefinding
obo:is about
mci:anatomical structure
_:if01 _:as01
radlex:wall of sigmoid colon
Radlex:sigmoidcolon
radlex:gastrointestinal tract
radlex:distal part of sigmoid colon
SELECT ?fWHERE {
SERVICE <http:localhost:3030/radlex/sparql>{ SELECT ?partOfGastroIntest
WHERE {# part of the gastroint. tract?partOfGastroIntest radlex:Part_Of+ radlex:RID94 .
}}
?f a obo:OGMS_0000014; # a obo:clinical findingobo:IAO_0000136 [ # which is about
# some part of the gastroint. tracta ?partOfGastroIntest].
}
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SPARQL Update Queries
Normal and abnormal status of findings about lymph nodes.
INSERT {?f a ?findingType .
}WHERE {
SERVICE <http:localhost:3030/radlex/sparql>{ SELECT ?lymphNode
WHERE {?lymphNode rdfs:subClassOf radlex:RID13296 .
}}
?f a obo:OGMS_0000014; # a obo:clinical findingobo:IAO_0000136 [ a ?lympNode ]; # which is about some lymph node(mci:MCI_0000056 | mci:MCI_0000057) [ # approximately or preciselya obo:IAO_0000408 ; # a length measurement datumobo:IAO_0000039 uo:UO_0000015; # has measurement unit label uo:centimeterobo:IAO_0000004 ?v; # has measurement value ?v
] .BIND( IF(?v <= 1),
mci:MCI_0000034, # normal findingmci:MCI_0000035 ) # abnormal findingAS ?findingType)
}
mci:approximately
obo:length measurement
datum
0.8^^xsd:decimal
obo:has measurement unit label
obo:imagefinding radlex:lymph
node of thorax
obo:is about
mci:anatomical structure
obo:has measurement value
_:if01 _:as01
_:lmd01uo:centimeter
obo:normalfinding
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Semantic Model for Clinical Information
Agenda
Clinical Data and Problem Description
The Model for Clinical Information
Storage and Queries
Related Work
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Related Work (I)
Existing open data models.
Model Motivation Status Difference to MCI
HL7 Reference Information Model v3
• supplement to standards for messaging and clinical documents (CDA)
• 2010: first owl-version• inconsistent definitions of concepts
• MCI is based on established upper level ontologies
IHE • cross institutional EHRs • mainly for data exchange• based on standards likeHL7, XACML, SAML• granular access rights
OpenEHR
• interoperability and computability in e-health• formal representation of clinial data
• archetypes and templates• use of external terminologies
• not in RDF
i2b2 SMART Data Model • supplement to the i2b2 datawarehouse
• work in progress• should be the basis for i2b2 SMART applications
• MCI is based on established upper level ontologies
Translational Medicine Ontology (TMO)
• data integration for clinical studies
• actively worked on by the W3C HCLS interest group
• focus on studies, especially with relation to omics data
Computer based Patient Record (CPR)
• creation of a semantic patient model based on BFO and BioTop
• last modification in July 31th
• MCI builds upon OGMS• BioTop is not included
… … … …
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Related Work (II)
Existing data models from horizontal IT providers.
Model Motivation Status Difference to MCI
Oracle Healthcare Data Model
• DWH• implement healthcare specific KPIs for reporting
• extended model for BI• many temp. concepts• reporting
• more concepts (many for adm.)• focus on hospital organization
Teradata HealthcareLogical Data Model
• DWH• framework for knowledge applications
• “single view of your enterprise”• strategic business and clinical information
• administration centric• focus on the hospitals organization
IBM Healthcare Provider Data Model
• DWH• health analytics• business insights• „innovate and improve the cost, quality and experience of healthcare”
• connection to IBM warehousing platforms and BI applications• application modules for “patient care and insights”
• administration centric• focus on hospital organization
… … … …
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Heiner OberkampfPhD student
Corporate TechnologyCT RTC BAM KMR-DE
Otto-Hahn-Ring 681739 München, Deutschland
Tel: +49 89 636-37566Fax: +49 89 636-49438Mobile: +49 176 49839029
E-mail:[email protected]
Questions ?