Clinical and Translational Science Ontology Workshop
- Tutorial: April 24, 2012 -- PLACES ONCE AGAIN AVAILABLE
- Workshop: April 25-26, 2012
Organization: Barry Smith (NCBO / Buffalo), Jessica Tenenbaum (Duke), Rob Wynden (UCSF)
Registration: Please write to Barry Smith
The Tutorial will provide an introduction to ontology methods and technology for students and researchers. Topics highlighted in the tutorial will be of particular interest to individuals at institutions conducting clinical and translational research, including institutions which have or are interested in obtaining CTSA (Clinical and Translational Science Award) grants.
The Workshop will provide an opportunity for those involved in ontology-related projects in the field of clinical and translational science to present on-going work and to review what has been achieved thus far. It will conclude with consideration of plans and strategies for enhanced coordination of ontology development initiatives in the field of clinical and translational science in the future.
Tutorial: Tuesday, April 24, 10.30am-6pm
- We have secured a larger room for the TUTORIAL; places are once again available
To register, please write to email@example.com.
For tutorial schedule and logistics information see here
Workshop Day 1: Wednesday, April 25, 9am-5pm (Draft Schedule)
- Morning: The NCBO and Clinical and Translation Science Ontologies
- Mark Musen (Stanford / NCBO): Introduction and Welcome
- Nigam Shah (Stanford / NCBO): Making Sense of Unstructured Data in Medicine using Ontologies: An Overview of NCBO Technology
- Changes in biomedical science, public policy, information technology, and electronic heath record (EHR) adoption have converged recently to enable a transformation in the delivery, efficiency, and effectiveness of health care. The true richness and complexity of health records lies within the clinical notes, which are free-text reports written by doctors and nurses in their daily practice. We have developed a scalable annotation and analysis workflow that uses public biomedical ontologies and is based on the term recognition tools developed by the National Center for Biomedical Ontology (NCBO). For further details see here
- Chris Chute (Mayo): Data Governance and Normalization within the Mayo Clinic Enterprise
- The principles and practice of data governance, as undertaken across the Mayo Clinic enterprise, will be reviewed and discussed, with particular emphasis on vocabulary harmonization and practice using NCBO tools for research. Extension to larger-scale consortia including the ONC HIT Standards Committee,SHARPn, ISO, and CIMI will be considered.
- Shawn Murphy (Partners): Developing i2b2 Ontologies for the Long Haul
- In i2b2, we are developing methods and tools to help investigators answer research questions using secondary clinical data using a diversity of coded data from billing, decision support, laboratory, and electronic medical records, which can also be combined with data from clinical trials and high-throughput genomic instruments. Looking forward, the coding systems are potentially evolving and changing and yet data will need to be retained in their original coding systems to assure no loss in fidelity. Genomic data has similar challenges from the changing nomenclature and gene coordinates that may alter how similar genetic variants are named over time. We are pursuing a strategy with the National Center for Biomedical Ontology (NCBO) to allow a smooth adaption from one coding system to another so that queries remain valid despite these coding changes. New coding systems and mappings can be presented through NCBO web services, and these are transformed into hierarchies that reflect not only the sibling mappings but also the possibly broader and narrower meanings of the mapped terms.
- Afternoon: Major Ontology Initiatives relevant to Clinical and Translational Research
- Joseph M. Gunnels and Jihad S. Obeid (Medical University of South Carolina): An Ontology for Informed Consents and Other Research Permissions
- During the development of a comprehensive system for managing informed consents and permissions for research involving human participants, considerable effort is dedicated to laying down the foundation for a semantic web infrastructure and an underlying ontology to help standardize the development of electronic informed consents and the capture of underlying data. The objective is to allow future connections with other semantic web applications and pooling of data from multiple research projects.
- Melissa Haendel (Oregon) and Jon Corson-Rikert (Cornell): CTSAConnect: The VIVO and eagle-i Ontology Initiatives
- VIVO is a platform for managing researcher profiles at or across institutions in support of research expertise location. Eagle-i is a system that enables researchers to share and search for research resources. ShareCenter facilitates grass-roots sharing of information about expertise, activities, and organizational resources within the CTSAs and related organizations. All of these platforms enable publication of Linked Open Data. We describe the CTSAconnect project to harmonize and develop ontologies from these and other sources to support complex queries regarding expertise and activities of investigators, physicians, biomedical research resources, services, and clinical activities in the translational science domain.
- Jessica Tenenbaum (Duke): Ontologies for Omic-Scale Datasets
- The use of "omic"-scale biomarkers to enable personalized medicine is becoming an increasingly important facet of translational biomedical research. Ontologies such as the Gene Ontology (GO) and the Ontology for Biomedical Investigations (OBI) can be used to facilitate data sharing, re-use, integration, and querying. This presentation will cover some of the more commonly used ontologies and how they may be applied to these ends.
- Harold Lehmann (Baltimore): The Human Studies Database (HSDB) and the Ontology of Clinical Research (OCRe)
- Current efforts aimed at “tagging” research focus on study execution and results reporting. There is no existing ontology, however, for research methodology. Such an ontology would speed systematic reviews, aid in the assembly of research teams, and enable methodology-based decision support, among other use cases. The Ontology for Clinical Research is a national effort led by Ida Sim from UCSF currently validating its typology and implementing a number of OCRe-based tools. We will contrast this ontology with the Ontology for Biomedical Investigations and with the schemas of ClinicalTrials.gov and BRIDG.
- Richard Scheuermann and Lindsay Cowell (Dallas): NLP-Based Mapping of Textbook Pathology to Ontology for General Medical Science (OGMS)
- Information about disease pathogenesis and disease course is available almost exclusively as free text and is therefore not easily accessible for query and analysis. We are addressing this problem by developing an ontology-driven NLP system for the mapping of basic pathology knowledge from free text to terms from OBO Foundry ontologies. In particular, we are using the Ontology of General Medical Science, with its tripartite structure of disease-disorder-disease_course, as the basic framework for the system. We will discuss preliminary results and describe planned use cases.
Workshop Day 2: Thursday, April 26, 9am-4pm (Draft Schedule)
- 9:00am EHR, Ontology and Interoperability
- Rob Wynden (UCSF): The CTSA Health Ontology Mapper (HOM).
- The CTSA Health Ontology Mapper is an open source project to translate locally encoded patient encounter data, claims data and notes into standard biomedical terminologies by leveraging a real-time integration with the NCBO BioPortal REST services for access to biomedical ontologies and maps.
- William Hogan (Arkansas): Referent Tracking and Demographic Data Ontology
- Numerous problems in the field of ontology are the consequence of (1) failure to pay attention to the instances about which we are collecting data, and (2) the lack of formal mechanisms to track instances and instance data, to relate instance data properly to representational units in ontologies, and to represent all these things unambiguously in the context of EHR data. This presentation will demonstrate Referent Tracking as a solution to these deficiencies with application to various particular use cases such as demographics data.
- William Hogan (Arkansas): Referent Tracking and Demographic Data Ontology
- 12:00pm Lunch
- 1:00pm Next Steps
- How can we measure the value brought by ontology-based approaches?
- How can we ensure high-quality and high-value approaches?
- How can we promote a consistent approach across the CTSA consortium?
Theodora Bakker (New York University Langone Medical Center)
Olga Brazhnik (National Center for Advancing Translational Sciences, NIH)
Mathias Brochhausen (Translational Research Institute, University of Arkansas for Medical Sciences)
Ling Chin (NIAID/DIADS, NIH)
Chris Chute (Mayo / NCBO)
Elaine Collier (National Center for Advancing Translational Sciences, NIH)
Lindsay Cowell (North and Central Texas Clinical and Translational Science Initiative / University of Texas Southwestern Medical Center at Dallas)
Alexander Diehl (University at Buffalo)
David Eichmann (Institute for Clinical and Translational Science, University of Iowa)
Michael Ferrante (University of Wisconsin Medical Foundation)
Davera Gabriel (Clinical & Translational Science Center, University of California, Davis)
Solomon T. Garner, Jr. (Jackson State University / RCMI Translational Research Network Data Technology Coordinating Center)
Carmelo Gaudioso (Roswell Park Cancer Institute, Buffalo)
Peter Good (National Human Genome Research Institute, NIH)
Joseph M. Gunnels (Medical University of South Carolina)
Melissa Haendel (Oregon Health & Science University / CTSA Connect)
Karen Hanson (New York University Langone Medical Center)
Daniel Harris (University of Kentucky Center for Clinical and Translational Science)
Kathleen Hayden (University of Michigan Health System – Medical Center Information Technology)
Darren Henderson (University of Kentucky Center for Clinical and Translational Science)
William Hogan (Translational Research Institute, University of Arkansas for Medical Sciences)
Pankaj Jaiswal (Oregon State University)
Pathak Jyotishman (Mayo Clinic / NCBO)
Warren Kibbe (Northwestern University Clinical and Translational Sciences Institute)
James Law (University of Michigan)
Harold Lehmann (Johns Hopkins / Institute for Clinical and Translational Research
Donald A. McClain (University of Utah Center for Clinical & Translational Science)
Eric Meeks (University of California at San Francisco)
Shawn Murphy (Partners Healthcare Research Computing / Harvard Medical School)
Mark Musen (Stanford Center for Biomedical Informatics Research / NCBO)
M. Theresa Perry (RCMI TRN Data and Technology Coordinating Center, Jacksonville State University)
Taylor Pressler (The Ohio State University Center for Clinical and Translational Science)
Mark Ressler (University at Buffalo)
Michael Sayre (National Institute on Minority Health and Health Disparities, NIH)
Jody Sachs (National Center for Advancing Translational Sciences, NIH)
Richard Scheuermann (North and Central Texas Clinical and Translational Science Initiative / University of Texas Southwestern Medical Center at Dallas)
Amitava Shee (University of Michigan / Michigan Institute for Clinical & Health Research)
Nigam Shah (Stanford Center for Biomedical Informatics Research / NCBO)
Barry Smith (University at Buffalo / NCBO)
Dagobert Soergel (University at Buffalo)
Shumei S. Sun (Virginia Commonwealth University / CTSA Biomedical Informatics Core)
Alisa Surkis (New York University School of Medicine)
Umberto Tachinardi (University of Wisconsin Medical Foundation)
Jessica Tenenbaum (Duke Translational Medicine Institute)
Carlo Torniai (Oregon Health & Science University / CTSA Connect)
David Towers (University of Wisconsin Medical Foundation)
Patricia Whetzel (Stanford University / NCBO)
Rob Wynden (University of California at San Francisco)