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This article discusses the application of ontology and controlled vocabularies in clinical trials, with a focus on the SPIROMICS study. It explores the use cases, role of ontology in SPIROMICS, controlled vocabulary management, and the future of this field.
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Ontology and Controlled Vocabulary in Clinical Trials JavedMostafa Jane Greenberg RahulDeshmukh Lina Huang
Outline • Clinical Trials • SPIROMICS • Application of Ontologies and Controlled Vocabularies • Use Cases - Ontologiesin Clinical Trials • SPIRO-V : Role of Ontology and Controlled Vocabularies in SPIROMICS • SPIRO-V: Where We Are Now • Controlled Vocabulary Management - The Road Ahead • Demo • Questions?
Clinical Trials • Conducted by Government Organizations, Pharmaceutical Companies, Academic Research Centers etc. • Mostly to assess safety and effectiveness of new medication or device • Types • Treatments - Combination of drugs • Diagnostics • Quality of Life - For patients with chronic illness
Clinical Trials (Contd.) • Phases • Phase 0 - Protocol , Patient Identification • Phase 1 – Small Group (20-80) safety & side – effects of drug/treatment • Phase II – Larger Group (100-300) • Phase III – Large Group (1000-3000) • Phase IV – Drug’s Risks, Benefits and Optimal Uses • Duration – 6 to 8 years • Cost for pharmaceutical companies between $100 - $800 Million • In 2005, 8000 Clinical Trials, $24 Billion Invested
SPIROMICS • Subpopulations and intermediate outcome measures in COPD study (SPIROMICS) • Primary Goals • Identify and validate markers of disease severity • Identify disease subpopulations • Secondary Goals • Clarify the natural history of COPD • Develop bioinformatics infrastructure • Generate clinical, radiographic and genetic data that can be used for future multisite clinical trials
Application of Ontologies and Controlled Vocabularies Ontology , Controlled Vocabulary Communication Indexing Functions For people to talk the same language To improve retrieval and analysis of data Retrieval Browsing Visualization
Use Cases - Ontologies in Clinical Trials • Locating eligible patients for clinical trials – IBM, Columbia University • Matched patient data to SNOMED-CT ontology • Semantic gulf between raw data and clinician’s interpretation • Structural representation of a disease ontology – Influenza Infectious Disease Ontology • Coverage of Infectious Disease Domain • Clinical Trial Data Management System – CancerGrid • Model of study OR Dataset Forms, Services, Metadata Registry etc
SPIRO-V: Vision SPIRO-V Clinical Trial Application Ontology Visualization Patient Identification Specimen Tracking SPIROMICS Knowledge Base Ontology Clinical Trial DBMS Mapping Controlled Vocabularies Editing/ Management SPIROMICS Controlled Vocabularies
Where We Are Now • Controlled Vocabularies Harvesting • Goal - A COPD Vocabulary set to accurately describe all the SPIROMICS cohorts, phenotypes, and outcome measures. • Two Approaches • Manual • Automatic
Manual Approach • Manual approach to collect vocabularies from authoritative sources on COPD • Domain experts conduct quality control • Downside: • Low efficiency • communication, coordination takes time
Automatic Approach • A relational database back end to store the terms, definitions and associations • Incorporation of VCGS automatic metadata generation system for rapid harvesting • Provide human review functionality to control the quality of terms and associations generated. • Manage controlled vocabularies development process
Manage Controlled Vocabulary • Visualize vocabulary set and make it browsable, searchable, and editable. • Vocabulary gathering workflow: • Suggest candidates -> review -> release/reject • Collaborative initiatives: co-authoring and discussion
Demo • Database • Physical database in MySQL • VCGS • TemaTres • SPIRO-V
References • http://clinicaltrials.gov/ct2/home • http://www.cscc.unc.edu/spir/ • http://iswc2007.semanticweb.org/papers/809.pdf • http://influenzaontologywiki.igs.umaryland.edu/wiki/index.php/Main_Page • http://www.cancergrid.org/
Collaborative thesaurus editing • Level of user privileges • Common users can suggest a candidate term, an association or a definition and provide feedback • Authorized users can reject/accept the suggestions. • Document changes and comments from different users.
Ontology, Thesaurus, Controlled Vocabularies Ontology Thesaurus Where We Are Controlled Vocabularies