Amia 2013: From EHRs to Linked Data: representing and mining encounter data for clinical expertise...
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Transcript of 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
www.ctsaconnect.org CTSAconnectReveal Connections. Realize Potential.
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
www.ctsaconnect.org CTSAconnectReveal Connections. Realize Potential.
ISF Clinical module: encounter
ARG: Agents, Resources, Grants ontologyCM: Clinical moduleOGMS: Ontology for General Medical ScienceFOAF: Friend of a Friend vocabulary
www.ctsaconnect.org CTSAconnectReveal Connections. Realize Potential.
ISF Clinical module: encounter output
CM: Clinical moduleOBI: Ontology for Biomedical InvestigationsOGMS: Ontology for General Medical Science
www.ctsaconnect.org CTSAconnectReveal Connections. Realize Potential.
ISF: Clinical expertise representation
Leveraging billing codes to represent clinical expertise- expertise as “weights” associated to billing codes
www.ctsaconnect.org CTSAconnectReveal Connections. Realize Potential.
Computing and publishing clinical expertise
Step 1Aggregate
Clinical Data
Step 2Compute Expertise
Step 4Publish Linked
Data
Step 3Map Data to
ISF
www.ctsaconnect.org CTSAconnectReveal Connections. Realize Potential.
Aggregate clinical data
Step 1Aggregate
Clinical Data
Step 2Compute Expertise
Step 4Publish Linked
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)
www.ctsaconnect.org CTSAconnectReveal Connections. Realize Potential.
Compute expertise: weighting the codes
Step 1Aggregate
Clinical Data
Step 4Publish Linked
Data
Step 2Compute Expertise
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
www.ctsaconnect.org CTSAconnectReveal Connections. Realize Potential.
Compute expertise: footprint
Step 1Aggregate
Clinical Data
Step 4Publish Linked
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
Step 2Compute Expertise
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
…. ….
…. ….
…. ….
www.ctsaconnect.org CTSAconnectReveal Connections. Realize Potential.
Mapping Expertise to the ISF
Step 1Aggregate
Clinical Data
Step 4Publish Linked
Data
Step 3 Map Data to
ISF
Step 2Map Data to
ISF
www.ctsaconnect.org CTSAconnectReveal Connections. Realize Potential.
Publish Linked Data
Step 1Aggregate
Clinical Data
Step 2Map Data to
ISF
Step 4Publish Linked
Data
Step 3Compute Expertise
Linked Data cloud
SPA
RQ
LEn
dp
oin
tsO
the
r A
PIs
…
Triple StoresSeveral means to access and
query data
www.ctsaconnect.org CTSAconnectReveal Connections. Realize Potential.
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
www.ctsaconnect.org CTSAconnectReveal Connections. Realize Potential.
Sample encounter data published as LOD
Inferred Types
Annotations and Properties
Health Care Encounter Instance URI
www.ctsaconnect.org CTSAconnectReveal Connections. Realize Potential.
Querying the sample encounter data
www.ctsaconnect.org CTSAconnectReveal Connections. Realize Potential.
Next steps: enhance expertise representation by mapping ICD9 to MeSH
www.ctsaconnect.org CTSAconnectReveal Connections. Realize Potential.
Next steps: enhance expertise calculation
• More sophisticated algorithm leveraging MeSHhierarchy
www.ctsaconnect.org CTSAconnectReveal Connections. Realize Potential.
Beyond expertise
Expertise linked to MeSH will enable meaningful connections between clinicians, basic researchers, and biomedical knowledge
www.ctsaconnect.org CTSAconnectReveal Connections. Realize Potential.
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