From billing codes to expertise: mining, representing and sharing clinical research profiles in the Linked Data Cloud
Upcoming SlideShare
Loading in...5
×
 

From billing codes to expertise: mining, representing and sharing clinical research profiles in the Linked Data Cloud

on

  • 723 views

Usage of EHR billing code to identify Clinical Expertise

Usage of EHR billing code to identify Clinical Expertise

Statistics

Views

Total Views
723
Views on SlideShare
719
Embed Views
4

Actions

Likes
0
Downloads
4
Comments
0

2 Embeds 4

https://twitter.com 3
https://si0.twimg.com 1

Accessibility

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment
  • Need to have nice picture here about the concept expressed.. Maybe it would be great to have an actual example about connecting ISF expertise data with other data ( I can use some SAPRQL queries)For this is required clear semantics and that’s why we need RDF and OWL

From billing codes to expertise: mining, representing and sharing clinical research profiles in the Linked Data Cloud From billing codes to expertise: mining, representing and sharing clinical research profiles in the Linked Data Cloud Presentation Transcript

  • From billing codes to expertise: mining, representing and sharing clinical research profiles in the Linked Data Cloud Carlo TorniaiShahim Essaid, Chris Barnes, Stephen Williams, Janos Hajagos Nicole Vasilevsky, Melissa Haendel
  • CTSAConnect ProjectNeeds: – 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 – more effective linking of information about clinicians and basic science researchers – publication of expertise data as Linked Data (LD) for use in other applicationswww.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  • Integrating VIVO and eagle-i VIVO eagle-i  VIVO is an ontology-driven application . . . for collecting and displaying information about people  eagle-i is an ontology-driven application . . . for collecting and searching research resources  Both publish Linked Data. Neither addresses clinical expertisewww.ctsaconnect.org 8/23/2012 CTSAconnect 3 Reveal Connections. Realize Potential.
  • Extending eagle-i and VIVO to represent clinical expertise Semantic VIVO Clinical eagle-i activities Researcher Characterization Clinician Characterization• Organizational affiliations • Research resources • Training and credentials• Grant and project participation – Reagents • Clinical research topic• Activities – Biospecimens • Specialization inferred from EHR – Teaching courses – Animal models – Procedures – Mentoring students – Instruments – Diagnosis – (Co)-authoring publications – Techinque – Prescriptions CTSAconnect will produce a single Integrated Semantic Framework that includes clinical expertise www.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  • 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.
  • 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.
  • 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.
  • Collecting and publishing clinical expertise as represented by encounter Step 1 Step 2 Step 3 Step 4 Aggregate Map Data to Compute Publish Linked Clinical Data ISF Expertise Datawww.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  • Aggregate clinical data Step 1 Step 2 Step 3 Step 4 Aggregate Map Data to Compute Publish Linked Clinical Data ISF Expertise 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.
  • Map data to ISF Step 1 Step 2 Step 3 Step 4 Aggregate Map Data to Compute Publish Linked Clinical Data ISF Expertise Data Java scripts RDF UniqueProvider ID ICD Code Value Code Count Patient Count Code Label OWL API triples Unilateral or unspecified femoral hernia with 1234567 552.00 1 1 obstruction (ICD9CM 552.00) Bilateral femoral hernia without mention of 1234567 553.02 8 6 obstruction or gangrene (ICD9CM 553.02) Regional enteritis of large 1234567 555.1 4 1 intestine (ICD9CM 555.1) Corrected transposition of 1234568 745.12 10 5 great vessels (ICD9CM 745.12) Aggregated Clinical Data ISF www.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  • Compute Expertise Step 1 Step 2 Step 3 Step 4 Aggregate Map Data to Compute Publish Linked Clinical Data ISF Expertise 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 knowledgewww.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  • Compute Expertise: Mapping ICD9 to MeSHwww.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  • Compute Expertise: weighting Step 1 Step 2 Step 3 Step 4 Aggregate Map Data to Compute Publish Linked Clinical Data ISF Expertise 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 = 15www.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  • 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.
  • Sample encounter data published as LOD Health care encounter Annotations and Instance URI Properties Inferred Typeswww.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  • Querying the datawww.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  • 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 patternswww.ctsaconnect.org CTSAconnect Reveal Connections. Realize Potential.
  • Information  CTSAconnect project  Carlo Torniai torniai@ohsu.edu ctsaconnect.org  CTSAconnect ontology source  Shahim Essaid http://code.google.com/p/connect-isf/ essaids@ohsu.edu  The clinical module can be directed  Chris Barnes accessed at http://bit.ly/clinical-isf cpb@ufl.edu  Linked Data generation code http://bit.ly/isf-lod-code  Janos Hajagos janos.hajagos@stonybrook.edu  eagle-i federated search eagle-i.net  Stephen V Williams  VIVO integrated search swilliams@ctrip.ufl.edu vivosearch.org  Nicole Vasilevski  CTSA ShareCenter vasilevs@ohsu.edu ctsasharecenter.org  Melissa Haendel haendel@ohsu.eduCTSA 10-001: 100928SB23 www.ctsaconnect.org CTSAconnectPROJECT #: 00921-0001 Reveal Connections. Realize Potential.