From EHRs to Linked Data: representing and mining encounter      data for clinical expertise             evaluation       ...
CTSAConnect ProjectGoals:   – Identify potential collaborators, relevant resources, and     expertise across scientific di...
Merging VIVO and eagle-i                                 Semantic                People                                   ...
ISF Clinical module                                        ARG: Agents, Resources, Grants ontology                        ...
ISF Clinical module: encounter                                   ARG: Agents, Resources, Grants ontology                  ...
ISF Clinical module: encounter output CM: Clinical module OBI: Ontology for Biomedical Investigations OGMS: Ontology for G...
ISF: Clinical expertise representation  Leveraging billing codes to represent clinical expertise     - expertise as “weigh...
Computing and publishing clinical                    expertise     Step 1            Step 2       Step 3                  ...
Aggregate clinical data     Step 1                 Step 2                 Step 3                       Step 4   Aggregate ...
Compute expertise: weighting the codes       Step 1          Step 2            Step 3                    Step 4     Aggreg...
Compute expertise: footprint     Step 1                 Step 2       Step 3                    Step 4   Aggregate         ...
Mapping Expertise to the ISF     Step 1              Step 2        Step 3                    Step 4   Aggregate           ...
Publish Linked Data     Step 1               Step 2            Step 3                       Step 4   Aggregate            ...
What can be done with the published                       datasetSELECT ?expertise ?label ?weightWHERE{                   ...
Sample encounter data published as LOD                                      Health Care Encounter    Annotations and      ...
Querying the sample encounter datawww.ctsaconnect.org                  CTSAconnect                             Reveal Conn...
Next steps: enhance expertise   representation by mapping ICD9 to MeSHwww.ctsaconnect.org                  CTSAconnect    ...
Next steps: enhance expertise calculation• More sophisticated algorithm leveraging MeSH  hierarchywww.ctsaconnect.org     ...
Beyond expertise  Expertise linked to MeSH will enable meaningful connections  between clinicians, basic researchers, and ...
Team                                                      Resources                                                       ...
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Amia 2013: From EHRs to Linked Data: representing and mining encounter data for clinical expertise evaluation

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  • Synostosis: abnorm union between bones or parts of bonesSyndactyly: A congenital anomaly of the hand or foot, marked by the webbing between adjacent fingers or toes. Syndactylies are classified as complete or incomplete by the degree of joining. Syndactylies can also be simple or complex. Simple syndactyly indicates joining of only skin or soft tissue; complex syndactyly marks joining of bony elements.Craniosynostoses: Premature closure of one or more CRANIAL SUTURES. The sutures are the joints that exist between the skull bones after birth but later close or fuse together.Antley-Bixler Syndrome: An inherited condition characterized by multiple malformations of CARTILAGE and bone including CRANIOSYNOSTOSIS; midfacehypoplasia; radiohumeralSYNOSTOSIS; CHOANAL ATRESIA; femoral bowing; neonatal fractures; and multiple joint CONTRACTURES and, occasionally, urogenital, gastrointestinal or cardiac defects. In utero exposure to FLUCONAZOLE, as well as mutations in at least two separate genes are associated with this condition - POR (encoding P450 (cytochrome) oxidoreductase ( NADPH-FERRIHEMOPROTEIN REDUCTASE)) and FGFR2 (encoding FIBROBLAST GROWTH FACTOR RECEPTOR 2).The figure attempts to show how a weight for a specific concept could be partially passed up the inheritance hierarchy and merged with other values passed up the hierarchy from other concepts. The concept “syndactyly”, which is the mapping of the ICD9 code from the previous slide, is given a weight of 15 by considering the percentage of a clinician’s patients that have that code assigned and by augmenting that percentage with the frequency of use of this code. In other words, if the code is assigned more than once to a patient, the frequency will be more than 1 and this increased frequency should be used as an indication of a provider’s expertise in this area.The next step is to pass up the weight but avoid passing up the full weight in order to avoid having high scores along the whole path to the root concept. The figure shows one way for doing this where the fraction of the weight passed up is related to the number of sibling concepts. The fraction passed up is 1/3 for the concept “synostosis” because there only two other siblings in MeSH but the fraction to the other more general concept is 1/10 due to the existence of 9 siblings under that part of the hierarchy. This choice appears to be correct in this case because we would not want to assume that a clinician that is specialized in “syndactyly” is also specialized in all the various “congenital limb deformities” but the provider can be considered an expert in “synostosis” since “synostosis” is closer “syndactyly”. The assumption is that the closeness of a subconcept is related to the number of siblings; the more siblings there are, the broader or more distant the parent concept is assumed to be.
  • “syndactyly” is a variable fusion of digits (fingers or toes) with or without the fusion of bones. The original ICD codes is specific to the fingers with fusion of bones. MeSH doesn’t have that level of specificity so there is no direct mapping to MeSH. However, SNOMED-CT does provide this level of specificity and as in the case for the ICD code, there is no mapping of this SNOMEC code to MeSH.We can find mappings to the MeSH heading “Syndactyly” when we use more general (parent) ICD or SNOMED codes where the concept is “any fusion of fingers or toes with or without fusion of bones”. The figure shows two ways for reaching this more general concept, either by using a parent ICD code or by using SNOMED. The indirect mapping through SNOMED will be more necessary when the original coding system does not have a hierarchy or relations that enable the navigation to a more general concept. CPT codes are an example, they do not have a native hierarchy and the use of an alternative hierarchy will be needed.
  • 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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