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Finding Experts for Clinical Trials

Semantic Web for Healthcare and Life Sciences Workshop Beijing, China 22-04-2008. Finding Experts for Clinical Trials. Matthias Löbe, Roland Mücke and Christian Eder Institute of Medical Informatics, Statistics, and Epidemiology (IMISE), Universität Leipzig

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Finding Experts for Clinical Trials

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  1. Semantic Web for Healthcare and Life Sciences Workshop Beijing, China 22-04-2008 Finding Experts for Clinical Trials Matthias Löbe, Roland Mücke and Christian Eder Institute of Medical Informatics, Statistics, andEpidemiology (IMISE), Universität Leipzig matthias.loebe@imise.uni-leipzig.de

  2. What to expect? • Starting point: data integration of trial sites and investigators for a search engine • Problem: bad data quality, no common understanding of the domain • Solution: use semantic web technology • Method: • Develop a lightweight ontology • Program a harmonization tool • Code a search application • Outlook: can be extended for variety of use cases Semantic Web for Healthcare and Life Sciences 22-04- 2008 Matthias Löbe 2/12

  3. You start here Clinical Trial „Life Cycle“ Creation of study database Investigation of trial sites Creation of study protocol Regulative Registration Medical Problem Inclusion of Patients Data Entry and Monitoring Meta-Analysis and Epidemiology Analysis and Publication Archiving Semantic Web for Healthcare and Life Sciences 22-04- 2008 Matthias Löbe 3/12

  4. Terminology: 5 Views at a Trial Site • Level of granularity at which a trial site should be modeled Semantic Web for Healthcare and Life Sciences 22-04- 2008 Matthias Löbe 4/12

  5. Clinical Trial Domain Ontology Semantic Web for Healthcare and Life Sciences 22-04- 2008 Matthias Löbe 5/12

  6. Data Acquisition Tool Semantic Web for Healthcare and Life Sciences 22-04- 2008 Matthias Löbe 6/12

  7. Usage Example 1: Search for Experts Semantic Web for Healthcare and Life Sciences 22-04- 2008 Matthias Löbe 7/12

  8. Usage Example 2: Database of Investigator‘s CVs • Investigators need an qualifying CV (FDA form 1572) • Name and contact data • Experience record in clinical research • Audits of clinical trials • Scientific publications and additional qualifications • Trial sites need prove of expertise • Facilities data, staff list • Proof of eligibility and experience record • High costs for organizing CV data (especially which trials, on which indication) Semantic Web for Healthcare and Life Sciences 22-04- 2008 Matthias Löbe 8/12

  9. Future Work • Identification of suitable qualified trial sites • Calculate the right number of trial sites • Investigation for experts • Identifying experts, e.g. for medical guidelines • Syndication with study registers or web portals • Update central registers like clinicaltrials.gov or EudraCT • Study database creation • Generate a study protocol synopsis in CDISC ODM to import into a clinical trial management system Semantic Web for Healthcare and Life Sciences 22-04- 2008 Matthias Löbe 9/12

  10. Conclusion and Results • First use of Semantic Web technology in our group • Entry barrier is not too high • Data model proved flexible und distributable • 1.500 investigators and 700 trial sites integrated • Data quality greatly improved • Community feedback • 8,372 request/5,771 sessions in 2007 for Malignant Lymphoma • Reduced costs for study groups Semantic Web for Healthcare and Life Sciences 22-04- 2008 Matthias Löbe 10/12

  11. Outlook • Improve the ontology • Add constructs, review roles and properties • Use OWL • More expressive, cardinality, reasoning, rules • Use existing vocabularies • Certain concepts are covered by FOAF, SKOS, SIOC, ResumeRDF • Coordinate work with other efforts in that field • Information models like HL7 RIM, BRIDG, caBIG CTODS Semantic Web for Healthcare and Life Sciences 22-04- 2008 Matthias Löbe 11/12

  12. The End Thank you! Semantic Web for Healthcare and Life Sciences 22-04- 2008 Matthias Löbe 12/12

  13. Data Acquisition Tool Semantic Web for Healthcare and Life Sciences 22-04- 2008 Matthias Löbe 13/12

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