from billing codes to expertise: mining, representing and sharing clinical research profiles in the...

From billing codes to expertise: mining, representing and sharing

clinical research profiles in the Linked Data Cloud

Carlo Torniai

Shahim Essaid, Chris Barnes, Stephen Williams, Janos HajagosNicole Vasilevsky, Melissa Haendel

www.ctsaconnect.org CTSAconnectReveal Connections. Realize Potential.

CTSAConnect ProjectNeeds:

– Identify potential collaborators, relevant resources, and expertise across scientific disciplines

– Assemble translational teams of scientists to address specific research questions

Approach:

Create a semantic representation of clinician and basic science researcher expertise to enable

– more effective linking of information about clinicians and basic science researchers

– publication of expertise data as Linked Data (LD) for use in other applications

8/23/2012 3www.ctsaconnect.org CTSAconnectReveal Connections. Realize Potential.

Integrating VIVO and eagle-i

VIVO is an ontology-driven application . . . for collecting anddisplaying information about people

eagle-i is an ontology-driven application . . . for collecting and searching research resources

Both publish Linked Data. Neither addresses clinical expertise

eagle-i

VIVO


Extending eagle-i and VIVO to represent clinical expertise

eagle-i

VIVO

Semantic

Clinical activities

• Organizational affiliations

• Grant and project participation

• Activities

– Teaching courses

– Mentoring students

– (Co)-authoring publications

Researcher Characterization

• Research resources

– Reagents

– Biospecimens

– Animal models

– Instruments

– Techinque

• Training and credentials• Clinical research topic• Specialization inferred from EHR

– Procedures

– Diagnosis

– Prescriptions

Clinician Characterization

CTSAconnect will produce a single Integrated Semantic Framework that includes clinical expertise


ISF Clinical module

ARG: Agents, Resources, Grants ontologyCM: Clinical moduleIAO: Information Artifact OntologyOBI: Ontology for Biomedical InvestigationsOGMS: Ontology for General Medical ScienceFOAF: Friend of a Friend vocabularyBFO: Basic Formal Ontology


ISF Clinical module: encounter

ARG: Agents, Resources, Grants ontologyCM: Clinical moduleOGMS: Ontology for General Medical ScienceFOAF: Friend of a Friend vocabulary


ISF Clinical module: encounter output

CM: Clinical moduleOBI: Ontology for Biomedical InvestigationsOGMS: Ontology for General Medical Science


Collecting and publishing clinical expertise as represented by encounter

Step 1Aggregate

Clinical Data

Step 2Map Data to

ISF

Step 4Publish Linked

Data

Step 3Compute Expertise


Aggregate clinical data

Step 1Aggregate

Clinical Data

Step 2Map Data to

ISF


Data


Provider ID

ICD Code Value

Code Count

Unique Patient Count Code Label

1234567 552.00 1 1Unilateral or unspecified femoral hernia

with obstruction (ICD9CM 552.00)

1234567 553.02 8 6Bilateral femoral hernia without mention

of obstruction or gangrene (ICD9CM 553.02)

1234567 555.1 4 1Regional enteritis of large intestine

(ICD9CM 555.1)

1234568 745.12 10 5Corrected transposition of great vessels

(ICD9CM 745.12)


Map data to ISF

Step 1Aggregate

Clinical Data

Step 2Map Data to

ISF


Data


Provider ID ICD Code Value Code Count

UniquePatient Count Code Label

1234567 552.00 1 1

Unilateral or unspecified femoral hernia with

obstruction (ICD9CM 552.00)

1234567 553.02 8 6

Bilateral femoral hernia without mention of

obstruction or gangrene (ICD9CM 553.02)

1234567 555.1 4 1Regional enteritis of large intestine (ICD9CM 555.1)

1234568 745.12 10 5Corrected transposition of

great vessels (ICD9CM 745.12)

AggregatedClinical Data

ISF

RDFtriples

Java scriptsOWL API


Compute Expertise

Step 1Aggregate

Clinical Data

Step 2Map Data to

ISF


Data


• Unified Medical Language System (UMLS) aggregates Medical Subjects Heading (MeSH) and other terminologies by linking them to UMLS concept unique identifiers (CUI)

• UMLS CUIs will be used to map ICD9 and CPT codes to MeSH

• Expertise indexed by MeSH will enable meaningful connections between clinicians, basic researchers, and biomedical knowledge


Compute Expertise: Mapping ICD9 to MeSH


Compute Expertise: weighting

Step 1Aggregate

Clinical Data

Step 2Map Data to

ISF


Data


• Provider X has 500 patients

• S/he has used Syndactyly(ICD9: 755.12) for 30 unique patients 75 times

Percentage of patients with code: 30/500*100 = 6%

Code frequency: 75/30 = 2.5

Code weight: 6 * 2.5 = 15


Publish Linked Data

Step 1Aggregate

Clinical Data

Step 2Map Data to

ISF


Data


Linked Data cloud

SPA

RQ

LEn

dp

oin

tsO

the

r A

PIs

…

Triple StoresSeveral means to access and

query data


Sample encounter data published as LOD

Inferred Types

Annotations and Properties

Health care encounter Instance URI


Querying the data


Beyond expertise

• Encounter data represented using ISF and published as Linked Data, in addition to enhance linkage between clinical and basic expertise, will enable integration with multiple datasets which could be used in a variety of ways to discover useful clinical associations and patterns


Information CTSAconnect project

ctsaconnect.org

CTSAconnect ontology source

http://code.google.com/p/connect-isf/

The clinical module can be directed accessed at http://bit.ly/clinical-isf

Linked Data generation code http://bit.ly/isf-lod-code

eagle-i federated search

eagle-i.net

VIVO integrated search

vivosearch.org

CTSA ShareCenter

ctsasharecenter.org

CTSA 10-001: 100928SB23PROJECT #: 00921-0001

Carlo [email protected]

Shahim [email protected]

Chris [email protected]

Janos [email protected]

Stephen V [email protected]

Nicole [email protected]

Melissa [email protected]

http://www.ctsaconnect.org






http://bit.ly/clinical-isf




http://bit.ly/isf-lod-code






https://www.eagle-i.net/



http://www.vivoweb.org

ctsasharecenter.org

mailto:[email protected]











from billing codes to expertise: mining, representing and sharing clinical research profiles in the...

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