amia 2013: from ehrs to linked data: representing and mining encounter data for clinical expertise...

From EHRs to Linked Data: representing and mining encounter

data for clinical expertise evaluation

Carlo Torniai

Shahim Essaid, Chris Barnes, Mike Conlon, Stephen Williams, Janos Hajagos, Erich Bremer, Jon Corson-Rikert, Melissa Haendel

www.ctsaconnect.org CTSAconnectReveal Connections. Realize Potential.

CTSAConnect ProjectGoals:

– 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

– Broad and computable representation of translational expertise

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

3/26/2013 3www.ctsaconnect.org CTSAconnectReveal Connections. Realize Potential.

Merging VIVO and eagle-i

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

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

Both publish Linked Data. Neither addresses clinical expertise.

CTSAconnect will produce a single Integrated Semantic Framework, a modular collection of ontologies — that also includes clinical expertise

eagle-i

Resources

VIVO

People

Coordination

eagle-iVIVO

Semantic

Clinical activities


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


ISF: Clinical expertise representation

Leveraging billing codes to represent clinical expertise- expertise as “weights” associated to billing codes


Computing and publishing clinical expertise

Step 1Aggregate

Clinical Data

Step 2Compute Expertise

Step 4Publish Linked

Data

Step 3Map Data to

ISF


Aggregate clinical data

Step 1Aggregate

Clinical Data



Data

Step 3Map Data to

ISF

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)


Compute expertise: weighting the codes

Step 1Aggregate

Clinical Data


Data


Step 3Map Data to

ISF

Code Weight = code frequency * percentage of patients

A provider with 500 patients has used Syndactyly (ICD9: 755.12) for 30 unique patients 75 times

Percentage of patients with code: 6%

Code frequency: 75/30 = 2.5

Code weight: 6 * 2.5 = 15


Compute expertise: footprint

Step 1Aggregate

Clinical Data


Data

We group the codes according to the top level ICD code and get the top 10 codes to generate the expertise footprint for each practitioner

Step 3Map Data to

ISF


ICD code Weight

366.1 24.42

250 24

366.9 18.4

250.2 19.2

…. ….

ICD code Weight

250 43.2

366 42.82

…. ….

…. ….

…. ….


Mapping Expertise to the ISF

Step 1Aggregate

Clinical Data


Data

Step 3 Map Data to

ISF

Step 2Map Data to

ISF


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


What can be done with the published dataset

SELECT ?expertise ?label ?weight

WHERE

{

<http://ohsu.dev.eagle-i.net/i/1235281379> obo:BFO_0000086

?expertise.

?expertise_measurement obo:IAO_0000221 ?expertise.

?expertise_measurement obo:ARG_2000012 ?label.

?expertise_measurement obo:IAO_0000004 ?weight.

}

Select the expertise for provider http://ohsu.dev.eagle-i.net/i/1235281379

Select the weight and the label for measurements relative to theexpertise

Select the weight and the label for measurements

The information is enough to represent clinical expertise as a tag cloud


Sample encounter data published as LOD

Inferred Types

Annotations and Properties

Health Care Encounter Instance URI


Querying the sample encounter data


Next steps: enhance expertise representation by mapping ICD9 to MeSH


Next steps: enhance expertise calculation

• More sophisticated algorithm leveraging MeSHhierarchy


Beyond expertise

Expertise linked to MeSH will enable meaningful connections between clinicians, basic researchers, and biomedical knowledge


Team

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

OHSU:

Melissa Haendel, Carlo Torniai, Nicole Vasilevsky, Shahim Essaid, Eric Orwoll

Cornell University:

Jon Corson-Rikert, Dean Krafft, Brian Lowe

University of Florida:

Mike Conlon, Chris Barnes, Nicholas Rejack

Stony Brook University: Moises Eisenberg, Erich Bremer, Janos Hajagos

Harvard University:Daniela Bourges-WaldeggSophia Cheng

Share Center:Chris Kelleher, Will Corbett, Ranjit Das, Ben Sharma

University at Buffalo:Barry Smith, DagobertSoergel

CTSAconnect project ctsaconnect.org

The clinical module source:http://bit.ly/clinical-isf

CTSAconnect ontology sourcehttp://code.google.com/p/connect-isf/

Dataset and queries documentationhttps://code.google.com/p/ctsaconnect/w/list

Resources

Support : NCATS through Booz Allen

Hamilton

CTSA 10-001: 100928SB23

http://www.ctsaconnect.org

http://bit.ly/clinical-isf



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






https://code.google.com/p/ctsaconnect/w/list

https://code.google.com/p/ctsaconnect/w/list

amia 2013: from ehrs to linked data: representing and mining encounter data for clinical expertise...

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