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Semantic Web Technologies as a Framework for Clinical Informatics Chimezie Ogbuji (CCF) Chris Pierce (CCF) Chris Deaton (C...
Me <ul><li>I work in the Heart and Vascular Institute at the Cleveland Clinic </li></ul><ul><li>We store and query patient...
Outline <ul><li>Relevant methods in clinical informatics </li></ul><ul><li>Traditional challenges in cohort identification...
What is Informatics? <ul><li>The science of information: </li></ul><ul><ul><li>Gathering  </li></ul></ul><ul><ul><li>Analy...
Bioinformatics <ul><li>Bioinformatics </li></ul><ul><ul><li>Discipline of gathering, analyzing, and representing the struc...
Medical Informatics <ul><li>Medical informatics:  </li></ul><ul><ul><li>Discipline of gathering, analyzing, and representi...
Cohort Studies <ul><li>Longitudinal study:  </li></ul><ul><ul><li>Research study that involves repeated observations of th...
Retrospective Cohort Studies <ul><li>Observational clinical study:  </li></ul><ul><ul><li>A longitudinal study that looks ...
Reasoning Methods in Biomedical Informatics
Areas of Applied Ontology <ul><li>Controlled vocabulary standards and management </li></ul><ul><li>Reporting and export of...
Challenges in  Traditional C ohort Identification <ul><li>Domain-specific criteria are conceived by researches who dialog ...
Patient Records <ul><li>Computer-based Patient Record:  </li></ul><ul><ul><li>An electronic patient record that resides in...
Patient Records Cont. <ul><li>Longitudinal patient record:  </li></ul><ul><ul><li>Patient records from different times, pr...
RDF Datasets <ul><ul><li>“ A SPARQL query is executed against an RDF Dataset which represents a collection of graphs. An R...
RDF Datasets Cont. <ul><li>Similar to a document collection in XPath 2.0 </li></ul><ul><li>The GRAPH operator can be used ...
SPARQL &Cohort Identification <ul><li>One named graph per patient record (a  patient record graph ) </li></ul><ul><li>Each...
Use of Named Graphs <ul><li>In our vocabulary, there are instances of PatientRecord, Operation, Patient, etc. </li></ul><u...
Use of Named Graphs Cont. <ul><li>Easy to parallelize computation and optimal for cohort querying </li></ul><ul><ul><li>Co...
 
Patient Record Ontology <ul><li>3974+ OWL Classes, 171 Object properties, and 217 Datatype properties </li></ul><ul><li>Di...
Ontology: Diagnoses
Ontology: Coronary Anatomy
Ontology: Pathogens
Ontologies: Family History
Integration with Cyc KB <ul><li>Patient record ontology is aligned to Cyc common sense ontology </li></ul><ul><li>Lexical ...
Semantic Research Assistant <ul><li>Cyc-based medical expert system for cohort identification </li></ul><ul><li>Natural-la...
“ Semantic Interface” <ul><li>OWL serves as the schema for a cohort’s SPARQL protocol service </li></ul><ul><li>SPARQL is ...
Screenshots
 
 
 
 
 
 
 
 
CycL Query (thereExists ?ID (thereExists ?PATIENT (and (cCFhasLeftAtriumDiameter ?CATH-OR-ECHO ?DISTANCE) (patientTreated ...
SPARQL Queries SELECT ?VAR0 ?VAR1 ?VAR2 ?VAR3 ?VAR4 ?VAR5 ?VAR6 WHERE { ?VAR0 ptrec:hasSex ptrec:Sex_female . ?VAR0 a ptre...
 
Challenges <ul><li>Representing negation in SPARQL is painfully cumbersome </li></ul><ul><ul><li>Patients who had X but no...
Challenges Cont. <ul><li>SPARQL specification doesn’t allow matching blank nodes by name </li></ul><ul><li>No sufficient, ...
Questions? <ul><li>Case Study: A Semantic Web Content Repository for Clinical Research </li></ul><ul><li>http://www.w3.org...
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Semantic Web Technologies as a Framework for Clinical Informatics

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Semantic Web Technologies as a Framework for Clinical Informatics

  1. 1. Semantic Web Technologies as a Framework for Clinical Informatics Chimezie Ogbuji (CCF) Chris Pierce (CCF) Chris Deaton (Cycorp) Semantic Technology Conference 16 June 2009
  2. 2. Me <ul><li>I work in the Heart and Vascular Institute at the Cleveland Clinic </li></ul><ul><li>We store and query patient populations as an RDF dataset </li></ul><ul><li>Ph.D student at Case Western Reserve University </li></ul><ul><li>Researching medical informatics methodology </li></ul>
  3. 3. Outline <ul><li>Relevant methods in clinical informatics </li></ul><ul><li>Traditional challenges in cohort identification </li></ul><ul><li>RDF dataset and managing patient populations </li></ul><ul><li>Our cohort identification system </li></ul><ul><li>Challenges with current standards </li></ul>
  4. 4. What is Informatics? <ul><li>The science of information: </li></ul><ul><ul><li>Gathering </li></ul></ul><ul><ul><li>Analysis </li></ul></ul><ul><ul><li>Representation </li></ul></ul><ul><li>Scientific method: </li></ul><ul><ul><li>Body of techniques for investigating phenomena, acquiring new knowledge, or correcting & integrating previous knowledge. </li></ul></ul>
  5. 5. Bioinformatics <ul><li>Bioinformatics </li></ul><ul><ul><li>Discipline of gathering, analyzing, and representing the structure / function of genes and proteins and correlating these to disease and population variation. </li></ul></ul>
  6. 6. Medical Informatics <ul><li>Medical informatics: </li></ul><ul><ul><li>Discipline of gathering, analyzing, and representing longitudinal patient studies in health and disease while providing decision support or predictive tools to assist in the diagnosis and prognosis of clinical patient care. </li></ul></ul>
  7. 7. Cohort Studies <ul><li>Longitudinal study: </li></ul><ul><ul><li>Research study that involves repeated observations of the same items over long periods of time. </li></ul></ul><ul><li>Cohort: </li></ul><ul><ul><li>Group of subjects — most often humans from a given population — characterized by the experience of an event in a particular time span. </li></ul></ul>
  8. 8. Retrospective Cohort Studies <ul><li>Observational clinical study: </li></ul><ul><ul><li>A longitudinal study that looks back in time </li></ul></ul><ul><li>Dependent on curated patient record content </li></ul><ul><li>We primarily do observational studies from our cardiothoracic patient registry </li></ul>
  9. 9. Reasoning Methods in Biomedical Informatics
  10. 10. Areas of Applied Ontology <ul><li>Controlled vocabulary standards and management </li></ul><ul><li>Reporting and export of patient record content for analysis and aggregation </li></ul><ul><li>Population-based research </li></ul><ul><ul><li>Identification of cohorts </li></ul></ul>
  11. 11. Challenges in Traditional C ohort Identification <ul><li>Domain-specific criteria are conceived by researches who dialog with DBA(s) </li></ul><ul><ul><li>DBAs translate this into joins, aggregation, text matching, etc. </li></ul></ul><ul><li>Mostly an exercise in navigation of data structure </li></ul><ul><li>Organization of content cannot easily evolve </li></ul>
  12. 12. Patient Records <ul><li>Computer-based Patient Record: </li></ul><ul><ul><li>An electronic patient record that resides in a system specifically designed to support users through availability of complete and accurate data, practitioner reminders and alerts, clinical decision support systems, links to bodies of medical knowledge, and other aids. </li></ul></ul>
  13. 13. Patient Records Cont. <ul><li>Longitudinal patient record: </li></ul><ul><ul><li>Patient records from different times, providers, and sites of care that are linked to form a lifelong view of a patient’s health care experience or a single patient record system with the same characteristics. </li></ul></ul>
  14. 14. RDF Datasets <ul><ul><li>“ A SPARQL query is executed against an RDF Dataset which represents a collection of graphs. An RDF Dataset comprises one graph, the default graph, which does not have a name, and zero or more named graphs, where each named graph is identified by an IRI.“ </li></ul></ul>
  15. 15. RDF Datasets Cont. <ul><li>Similar to a document collection in XPath 2.0 </li></ul><ul><li>The GRAPH operator can be used to scope query patterns to a particular graph or within all named graph </li></ul>
  16. 16. SPARQL &Cohort Identification <ul><li>One named graph per patient record (a patient record graph ) </li></ul><ul><li>Each patient record graph is allocated a URI </li></ul><ul><li>No significant cross-graph statements. </li></ul><ul><ul><li>Beyond cohort identification, most processing happens within a single patient record graph </li></ul></ul>
  17. 17. Use of Named Graphs <ul><li>In our vocabulary, there are instances of PatientRecord, Operation, Patient, etc. </li></ul><ul><li>PatientRecord resources share a URI with their containing graph </li></ul><ul><li>GRAPH operator can be used to optimize the search space </li></ul>
  18. 18. Use of Named Graphs Cont. <ul><li>Easy to parallelize computation and optimal for cohort querying </li></ul><ul><ul><li>Constraints in the first part of query are cross-graph while the second part are intra-graph </li></ul></ul>
  19. 20. Patient Record Ontology <ul><li>3974+ OWL Classes, 171 Object properties, and 217 Datatype properties </li></ul><ul><li>Diseases, findings, symptoms, medication, procedures, etc… </li></ul><ul><li>SHOIN(D) expressiveness </li></ul><ul><ul><li>OWL-DL </li></ul></ul>
  20. 21. Ontology: Diagnoses
  21. 22. Ontology: Coronary Anatomy
  22. 23. Ontology: Pathogens
  23. 24. Ontologies: Family History
  24. 25. Integration with Cyc KB <ul><li>Patient record ontology is aligned to Cyc common sense ontology </li></ul><ul><li>Lexical metadata are added to facilitate natural language processing </li></ul><ul><li>Cyc SKSI protocol was extended to support SPARQL </li></ul>
  25. 26. Semantic Research Assistant <ul><li>Cyc-based medical expert system for cohort identification </li></ul><ul><li>Natural-language driven interface composes logical queries </li></ul><ul><li>Queries are generated against a SPARQL Protocol service </li></ul><ul><li>Leverages ontology alignment </li></ul>
  26. 27. “ Semantic Interface” <ul><li>OWL serves as the schema for a cohort’s SPARQL protocol service </li></ul><ul><li>SPARQL is the query interlingua </li></ul><ul><li>The Cyc KB’s common sense ontology and NLP capabilities shield the researcher from SPARQL, RDF, and OWL </li></ul>
  27. 28. Screenshots
  28. 37. CycL Query (thereExists ?ID (thereExists ?PATIENT (and (cCFhasLeftAtriumDiameter ?CATH-OR-ECHO ?DISTANCE) (patientTreated ?CATH-OR-ECHO ?PATIENT) (cCFCCFID ?PATIENT ?ID) (isa ?CATH-OR-ECHO Echocardiogram) (patientTreated ?CATH-OR-ECHO ?PATIENT) (or (and (patientSex ?PATIENT MaleHuman) (greaterThan ?DISTANCE ( (Centi Meter) 4.2))) (and (patientSex ?PATIENT FemaleHuman) (greaterThan ?DISTANCE ( (Centi Meter) 3.8)))) (temporallyBetween-Inclusive ?CATH-OR-ECHO (MonthFn January (YearFn 2008)) (DayFn 15 (MonthFn March (YearFn 2008)))))))
  29. 38. SPARQL Queries SELECT ?VAR0 ?VAR1 ?VAR2 ?VAR3 ?VAR4 ?VAR5 ?VAR6 WHERE { ?VAR0 ptrec:hasSex ptrec:Sex_female . ?VAR0 a ptrec:Patient . ?VAR1 dnode:contains ?VAR0 . ?VAR1 a ptrec:PatientRecord . ?VAR1 dnode:contains ?VAR2 . ?VAR2 a ptrec:Event_evaluation_echocardiogram> . ?VAR2 ptrec:hasLeftAtriumDiameter ?VAR3 . FILTER (?VAR3 > xsd:float(3.8)) ?VAR2 dnode:contains ?VAR4 . ?VAR4 a ptrec:EventStartDate . ?VAR4 ptrec:hasDateTimeMax ?VAR5 . FILTER (?VAR5 > xsd:dateTime(&quot;2007-12-31T23:59:59&quot;)) FILTER (xsd:dateTime(&quot;2008-03-16T00:00:00&quot;) > ?VAR5) ?VAR0 ptrec:hasCCFID ?VAR6 . }
  30. 40. Challenges <ul><li>Representing negation in SPARQL is painfully cumbersome </li></ul><ul><ul><li>Patients who had X but not Y </li></ul></ul><ul><li>No equivalent of SQL’s IN operator </li></ul><ul><ul><li>Find patients who had a diagnoses of an myocardial infarction, renal failure, or atrial fibrillation </li></ul></ul>
  31. 41. Challenges Cont. <ul><li>SPARQL specification doesn’t allow matching blank nodes by name </li></ul><ul><li>No sufficient, readily-available medical record ontologies </li></ul><ul><ul><li>We created our own </li></ul></ul><ul><li>Protocol doesn’t easily support a way to abort running queries </li></ul>
  32. 42. Questions? <ul><li>Case Study: A Semantic Web Content Repository for Clinical Research </li></ul><ul><li>http://www.w3.org/2001/sw/sweo/public/UseCases/ClevelandClinic/ </li></ul><ul><li>Email [email_address] for (updated) copy of slides </li></ul>

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