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Amia 2013: From EHRs to Linked Data: representing and mining encounter data for clinical expertise evaluation

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Amia 2013: From EHRs to Linked Data: representing and mining encounter data for clinical expertise evaluation

  1. 1. 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
  2. 2. CTSAConnect ProjectGoals: – Identify potential collaborators, relevant resources, and expertise across scientific disciplines – Assemble translational teams of scientists to address specific research questionsApproach: 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 applicationswww.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  3. 3. Merging VIVO and eagle-i Semantic People VIVO VIVO Coordination Clinical eagle-i eagle-i activities Resources  eagle-i is an ontology-driven application . . . for collecting and searching research resources.  VIVO is an ontology-driven application . . . for collecting and displaying 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 expertisewww.ctsaconnect.org 3/26/2013 CTSAconnect 3 Reveal Connections. Realize Potential.
  4. 4. ISF Clinical module ARG: Agents, Resources, Grants ontology CM: Clinical module IAO: Information Artifact Ontology OBI: Ontology for Biomedical Investigations OGMS: Ontology for General Medical Science FOAF: Friend of a Friend vocabulary BFO: Basic Formal Ontologywww.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  5. 5. ISF Clinical module: encounter ARG: Agents, Resources, Grants ontology CM: Clinical module OGMS: Ontology for General Medical Science FOAF: Friend of a Friend vocabularywww.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  6. 6. ISF Clinical module: encounter output CM: Clinical module OBI: Ontology for Biomedical Investigations OGMS: Ontology for General Medical Sciencewww.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  7. 7. ISF: Clinical expertise representation Leveraging billing codes to represent clinical expertise - expertise as “weights” associated to billing codeswww.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  8. 8. Computing and publishing clinical expertise Step 1 Step 2 Step 3 Step 4 Aggregate Compute Map Data to Publish Linked Clinical Data Expertise ISF Datawww.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  9. 9. Aggregate clinical data Step 1 Step 2 Step 3 Step 4 Aggregate Compute Map Data to Publish Linked Clinical Data Expertise ISF Data Provider ICD Code Unique Patient ID Code Value Count Count Code Label Unilateral or unspecified femoral hernia 1234567 552.00 1 1 with obstruction (ICD9CM 552.00) Bilateral femoral hernia without mention 1234567 553.02 8 6 of obstruction or gangrene (ICD9CM 553.02) Regional enteritis of large intestine 1234567 555.1 4 1 (ICD9CM 555.1) Corrected transposition of great vessels 1234568 745.12 10 5 (ICD9CM 745.12)www.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  10. 10. Compute expertise: weighting the codes Step 1 Step 2 Step 3 Step 4 Aggregate Compute Map Data to Publish Linked Clinical Data Expertise ISF DataCode Weight = code frequency * percentage of patientsA provider with 500 patients has used Syndactyly (ICD9: 755.12) for 30unique patients 75 timesPercentage of patients with code: 6%Code frequency: 75/30 = 2.5Code weight: 6 * 2.5 = 15www.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  11. 11. Compute expertise: footprint Step 1 Step 2 Step 3 Step 4 Aggregate Compute Map Data to Publish Linked Clinical Data Expertise ISF 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 ICD code Weight ICD code Weight 366.1 24.42 250 43.2 250 24 366 42.82 366.9 18.4 …. …. 250.2 19.2 …. …. …. …. …. ….www.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  12. 12. Mapping Expertise to the ISF Step 1 Step 2 Step 3 Step 4 Aggregate Map Data to Map Data to Publish Linked Clinical Data ISF ISF Datawww.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  13. 13. Publish Linked Data Step 1 Step 2 Step 3 Step 4 Aggregate Map Data to Compute Publish Linked Clinical Data ISF Expertise Data Other APIs Endpoints SPARQL … Linked Data Several means Triple Stores to access and cloud query datawww.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  14. 14. What can be done with the published datasetSELECT ?expertise ?label ?weightWHERE{ Select the expertise for<http://ohsu.dev.eagle-i.net/i/1235281379> obo:BFO_0000086?expertise. provider http://ohsu.dev.eagle-i.net/i/1235281379 Select the weight and the label?expertise_measurement obo:IAO_0000221 ?expertise. for measurements relative to the expertise?expertise_measurement obo:ARG_2000012 ?label.?expertise_measurement obo:IAO_0000004 ?weight. Select the weight and the label} for measurementsThe information is enough to represent clinical expertise as atag cloudwww.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  15. 15. Sample encounter data published as LOD Health Care Encounter Annotations and Instance URI Properties Inferred Typeswww.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  16. 16. Querying the sample encounter datawww.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  17. 17. Next steps: enhance expertise representation by mapping ICD9 to MeSHwww.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  18. 18. Next steps: enhance expertise calculation• More sophisticated algorithm leveraging MeSH hierarchywww.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  19. 19. Beyond expertise Expertise linked to MeSH will enable meaningful connections between clinicians, basic researchers, and biomedical knowledgewww.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  20. 20. Team Resources CTSAconnect projectOHSU: Stony Brook University: ctsaconnect.orgMelissa Haendel, Carlo Torniai,Moises Eisenberg, Erich Bremer, Janos HajagosNicole Vasilevsky, Shahim Essaid, The clinical module source:Eric Orwoll http://bit.ly/clinical-isf Harvard University: Daniela Bourges-WaldeggCornell University: Sophia Cheng CTSAconnect ontologyJon Corson-Rikert, Dean Krafft, sourcehttp://code.google.com/p/connect-isf/Brian Lowe Share Center:University of Florida: Chris Kelleher, Will Dataset and queries documentationMike Conlon, Chris Barnes, Corbett, Ranjit Das, Ben https://code.google.com/p/ctsaconnect/w/listNicholas Rejack Sharma University at Buffalo: Barry Smith, Dagobert Soergel Support : NCATS through Booz Allen Hamilton CTSA 10-001: 100928SB23 CTSA 10-001: 100928SB23 www.ctsaconnect.org CTSAconnect PROJECT #: 00921-0001 Reveal Connections. Realize Potential.

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