Difference between revisions of "Clinical and Translational Science Ontology Workshop"

From NCBO Wiki
Jump to navigation Jump to search
m
 
(26 intermediate revisions by 2 users not shown)
Line 6: Line 6:
  
 
:'''Tutorial: April 24, 2012'''  
 
:'''Tutorial: April 24, 2012'''  
::=== SMALL NUMBER OF PLACES REMAINING. NOW SCHEDULED TO FINISH AT 6:00pm === ==
+
::=== NOW FULLY BOOKED === ==
  
 
:'''Workshop: April 25-26, 2012'''  
 
:'''Workshop: April 25-26, 2012'''  
Line 22: Line 22:
 
----
 
----
  
=='''Tutorial: Tuesday, April 24 · 9:30am-6:00pm'''==
+
=='''Tutorial: Tuesday, April 24 · 9:30am-5:30pm'''==
  
 
For tutorial schedule and logistics information see [[Tutorial: Introduction to Biomedical Ontology for Clinical and Translational Research | here]]
 
For tutorial schedule and logistics information see [[Tutorial: Introduction to Biomedical Ontology for Clinical and Translational Research | here]]
Line 28: Line 28:
 
To register, please write to [mailto:ontology@buffalo.edu ontology@buffalo.edu].
 
To register, please write to [mailto:ontology@buffalo.edu ontology@buffalo.edu].
  
=='''Workshop Day 1: Wednesday, April 25 · 8:00am-5:00pm'''==
+
=='''Workshop Day 1: Wednesday, April 25 · 8:00am-5:30pm'''==
  
 
::8:00am Registration & Continental Breakfast
 
::8:00am Registration & Continental Breakfast
Line 36: Line 36:
 
::9:00am Mark Musen (Stanford / NCBO): Introduction and Welcome
 
::9:00am Mark Musen (Stanford / NCBO): Introduction and Welcome
  
::9:30am Nigam Shah (Stanford / NCBO): [[Making Sense of Unstructured Data in Medicine using Ontologies]]: An Overview of NCBO Technology
+
::9:30am Nigam Shah (Stanford / NCBO): [[Making Sense of Unstructured Data in Medicine using Ontologies]]: An Overview of NCBO Technology [http://goo.gl/FAfHO Slides]
  
 
::::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 [[Making Sense of Unstructured Data in Medicine using Ontologies | here]]
 
::::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 [[Making Sense of Unstructured Data in Medicine using Ontologies | here]]
Line 42: Line 42:
 
::10:15am Refreshment Break
 
::10:15am Refreshment Break
  
::10:45am Chris Chute (Mayo): Data Governance and Normalization within the Mayo Clinic Enterprise
+
::10:45am Chris Chute (Mayo): Data Governance and Normalization within the Mayo Clinic Enterprise [http://ontology.buffalo.edu/smith/ppt/CTS-Ontology_2012/WorkshopDay1/Chute_Data_Gov_and_Norm.pptx Slides]
  
 
::::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.
 
::::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.
  
::11:30am Shawn Murphy (Partners): Developing i2b2 Ontologies for the Long Haul
+
::11:30am Lori C. Phillips (Partners): Developing i2b2 Ontologies for the Long Haul [http://ontology.buffalo.edu/smith/ppt/CTS-Ontology_2012/WorkshopDay1/Phillips_i2b2.ppt Slides]
  
 
::::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.
 
::::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.
Line 54: Line 54:
 
:'''Afternoon: Major Ontology Initiatives relevant to Clinical and Translational Research'''
 
:'''Afternoon: Major Ontology Initiatives relevant to Clinical and Translational Research'''
  
::1:15pm Melissa Haendel (Oregon) and Jon Corson-Rikert (Cornell): CTSA''Connect'': The VIVO and eagle-i Ontology Initiatives
+
::1:15pm Melissa Haendel (Oregon) and Jon Corson-Rikert (Cornell): CTSA''Connect'': The VIVO and eagle-i Ontology Initiatives [http://ontology.buffalo.edu/smith/ppt/CTS-Ontology_2012/WorkshopDay1/ctsaconnect.ncbo.pptx Slides]
  
 
::::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.
 
::::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.
  
::2:00pm Jihad S. Obeid (Medical University of South Carolina): An Ontology for Informed Consents and Other Research Permissions
+
::2:00pm Jihad S. Obeid (Medical University of South Carolina) and Adela Grando (UCSF): An Ontology for Informed Consents and Other Research Permissions [http://ontology.buffalo.edu/smith/ppt/CTS-Ontology_2012/WorkshopDay1/Obeid_RPMS_ontology.pdf Slides (Obeid)], [http://ontology.buffalo.edu/smith/ppt/CTS-Ontology_2012/WorkshopDay1/Adela_Grando_Permissions_Ontology.pdf Slides (Grando)]
  
 
::::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.
 
::::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.
Line 64: Line 64:
 
::2:45pm Refreshment Break
 
::2:45pm Refreshment Break
  
::3:15pm Jessica Tenenbaum (Duke): Ontologies for Omic-Scale Datasets
+
::3:15pm Jessica Tenenbaum (Duke): Ontologies for Omic-Scale Datasets [http://ontology.buffalo.edu/smith/ppt/CTS-Ontology_2012/WorkshopDay2/Tenenbaum-Ontologies%20for%20Omics.pdf Slides]
  
 
::::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.
 
::::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.
  
::4:00pm Harold Lehmann (Baltimore): The Human Studies Database (HSDB) and the Ontology of Clinical Research (OCRe)
+
::4:00pm Harold Lehmann (Baltimore): The Human Studies Database (HSDB) and the Ontology of Clinical Research (OCRe) [http://ontology.buffalo.edu/smith/ppt/CTS-Ontology_2012/WorkshopDay1/Lehmann_Human_Studies_Database.pptx Slides]
 
 
 
::::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.
 
::::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.
  
::4:45pm Richard Scheuermann and Lindsay Cowell (Dallas): NLP-Based Mapping of Textbook Pathology to Ontology for General Medical Science (OGMS)  
+
::4:45pm Richard Scheuermann and Lindsay Cowell (Dallas): NLP-Based Mapping of Textbook Pathology to Ontology for General Medical Science (OGMS) [http://ontology.buffalo.edu/smith/ppt/CTS-Ontology_2012/WorkshopDay1/ScheuermannCTSA%20Ontology_APR2012.pptx Slides (Scheuermann)] [http://ontology.buffalo.edu/smith/ppt/CTS-Ontology_2012/WorkshopDay1/Cowell_CTSA_NLP.pdf Slides (Cowell)]
  
 
::::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.  
 
::::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.  
  
 +
::5:30pm Close of Day 1
 
----
 
----
  
Line 87: Line 87:
  
 
::::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.
 
::::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.
 +
 +
::::These [http://ontology.buffalo.edu/smith/ppt/CTS-Ontology_2012/WorkshopDay2/Wynden_Health_Ontology_Mapper.ppt slides] outline the method created by Rob Wynden to overcome the problems created where patient data are encoded in heterogeneous proprietary coding systems.
  
 
::10:30am Refreshment Break
 
::10:30am Refreshment Break
  
::11:00am William Hogan (Arkansas): Referent Tracking and Demographic Data Ontology
+
::11:00am William Hogan (Arkansas): Referent Tracking and Demographic Data Ontology [http://ontology.buffalo.edu/smith/ppt/CTS-Ontology_2012/WorkshopDay2/Hogan_CTSAOntology_2012.pptx Slides]
 
::::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.
 
::::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.
  
Line 100: Line 102:
 
::::How can we ensure high-quality and high-value approaches?  
 
::::How can we ensure high-quality and high-value approaches?  
 
::::How can we promote a consistent approach to ontology across the CTSA consortium?
 
::::How can we promote a consistent approach to ontology across the CTSA consortium?
 +
 +
::[http://ontology.buffalo.edu/smith/ppt/CTS-Ontology_2012/WorkshopDay2/CTSOntology_Discussion_Document.docx Discussion Document]
 +
::[http://ontology.buffalo.edu/smith/ppt/CTS-Ontology_2012/WorkshopDay2/Smith_Resurrecting_SOWG.pptx Strategy for identifying reference ontologies]
  
 
::2:30pm Refreshment Break
 
::2:30pm Refreshment Break
Line 105: Line 110:
 
::2:45pm Next Steps (continued)
 
::2:45pm Next Steps (continued)
  
::4:00pm Close
+
::4:00pm Close of Workshop
 
----
 
----
  
Line 201: Line 206:
  
 
Tom Mish (University of Wisconsin - Madison Institute for Clinical and Translational Research)
 
Tom Mish (University of Wisconsin - Madison Institute for Clinical and Translational Research)
 +
 +
Ketty Mobed (University of California at San Francisco)
  
 
Shawn Murphy (Partners Healthcare Research Computing / Harvard Medical School)
 
Shawn Murphy (Partners Healthcare Research Computing / Harvard Medical School)
Line 211: Line 218:
  
 
M. Theresa Perry (RCMI TRN Data and Technology Coordinating Center, Jacksonville State University)
 
M. Theresa Perry (RCMI TRN Data and Technology Coordinating Center, Jacksonville State University)
 +
 +
Lori C. Phillips (Partners, Harvard)
  
 
Taylor Pressler (The Ohio State University Center for Clinical and Translational Science)
 
Taylor Pressler (The Ohio State University Center for Clinical and Translational Science)
Line 237: Line 246:
  
 
Dagobert Soergel (University at Buffalo)
 
Dagobert Soergel (University at Buffalo)
 
Nicholas H. Steneck (Research Ethics Program, University of Michigan / Michigan Institute for Clinical and Health Research)
 
  
 
Alisa Surkis (New York University School of Medicine)                   
 
Alisa Surkis (New York University School of Medicine)                   
Line 248: Line 255:
 
Carlo Torniai (Oregon Health & Science University / CTSA Connect)
 
Carlo Torniai (Oregon Health & Science University / CTSA Connect)
  
Patricia Whetzel (Stanford University / NCBO)
+
Anita Walden (Duke University)
 +
 
 +
Trish Whetzel (Stanford University / NCBO)
  
 
Rob Wynden (University of California at San Francisco)
 
Rob Wynden (University of California at San Francisco)

Latest revision as of 08:14, 28 April 2012

The National Center for Biomedical Ontology will hold a Clinical and Translational Science Ontology Tutorial and Workshop as part of its series of training and dissemination events.

Venue: Hilton Garden Inn Baltimore Airport Special Group Rate

Dates:

Tutorial: April 24, 2012
=== NOW FULLY BOOKED === ==
Workshop: April 25-26, 2012
=== NOW FULLY BOOKED === ==


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 · 9:30am-5:30pm

For tutorial schedule and logistics information see here

To register, please write to ontology@buffalo.edu.

Workshop Day 1: Wednesday, April 25 · 8:00am-5:30pm

8:00am Registration & Continental Breakfast
Morning: The NCBO and Clinical and Translation Science Ontologies
9:00am Mark Musen (Stanford / NCBO): Introduction and Welcome
9:30am Nigam Shah (Stanford / NCBO): Making Sense of Unstructured Data in Medicine using Ontologies: An Overview of NCBO Technology Slides
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
10:15am Refreshment Break
10:45am Chris Chute (Mayo): Data Governance and Normalization within the Mayo Clinic Enterprise Slides
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.
11:30am Lori C. Phillips (Partners): Developing i2b2 Ontologies for the Long Haul Slides
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.
12:15pm Lunch Break
Afternoon: Major Ontology Initiatives relevant to Clinical and Translational Research
1:15pm Melissa Haendel (Oregon) and Jon Corson-Rikert (Cornell): CTSAConnect: The VIVO and eagle-i Ontology Initiatives Slides
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.
2:00pm Jihad S. Obeid (Medical University of South Carolina) and Adela Grando (UCSF): An Ontology for Informed Consents and Other Research Permissions Slides (Obeid), Slides (Grando)
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.
2:45pm Refreshment Break
3:15pm Jessica Tenenbaum (Duke): Ontologies for Omic-Scale Datasets Slides
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.
4:00pm Harold Lehmann (Baltimore): The Human Studies Database (HSDB) and the Ontology of Clinical Research (OCRe) Slides
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.
4:45pm Richard Scheuermann and Lindsay Cowell (Dallas): NLP-Based Mapping of Textbook Pathology to Ontology for General Medical Science (OGMS) Slides (Scheuermann) Slides (Cowell)
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.
5:30pm Close of Day 1

Workshop Day 2: Thursday, April 26 · 8:30am-4:00pm

8:30am Continental Breakfast
EHR, Ontology and Interoperability
9:00am 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.
These slides outline the method created by Rob Wynden to overcome the problems created where patient data are encoded in heterogeneous proprietary coding systems.
10:30am Refreshment Break
11:00am William Hogan (Arkansas): Referent Tracking and Demographic Data Ontology Slides
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.
12:00pm Lunch Break
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 to ontology across the CTSA consortium?
Discussion Document
Strategy for identifying reference ontologies
2:30pm Refreshment Break
2:45pm Next Steps (continued)
4:00pm Close of Workshop

Participants

Pankaj Agarwal (Bioinformatics Shared Resource, Duke Cancer Institute)

Joel Amoussou (Focused eHealth Innovations, Columbia, MD)

Sivaram Arabandi (Smart Content Strategy, Elsevier)

Theodora Bakker (New York University Langone Medical Center)

Rimma Belenkaya (Albert Einstein College of Medicine / CTSA Research Informatics Core)

Aziz Boxwala (University of California at San Diego)

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)

Jon Corson-Rikert (Cornell University / VIVO / CTSAConnect)

Melanie Courtot (British Columbia Cancer Research Centre (BCCRC), Vancouver)

Lindsay Cowell (North and Central Texas Clinical and Translational Science Initiative / University of Texas Southwestern Medical Center at Dallas)

Alexander Cox (University at Buffalo)

Alexander Diehl (University at Buffalo)

Kristi R. Eckerson (Emory University / Atlanta CTSA Institute)

David Eichmann (Institute for Clinical and Translational Science, University of Iowa)

Sharon Elcombe (Mayo Clinic CTSA)

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)

Bob Gehrke (Mayo Clinic CTSA)

Peter Good (National Human Genome Research Institute, NIH)

Adela Grando (University of California at San Diego)

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 (Center for Genome Research and Biocomputing, Oregon State University)

Mark Jensen (University at Buffalo)

Pathak Jyotishman (Mayo Clinic / NCBO)

Alla Karnovsky (University of Michigan Computational Medicine and Bioinformatics)

Warren Kibbe (Northwestern University Clinical and Translational Sciences Institute)

Justin Lancaster (BiomedServer, Lebanon NH)

James Law (University of Michigan)

Harold Lehmann (Johns Hopkins / Institute for Clinical and Translational Research

Michael Lin (Mayo Clinic CTSA)

Aenoch Lynn (Duke Biobank, Duke Translational Medicine Institute)

Donald A. McClain (University of Utah Center for Clinical & Translational Science)

Eric Meeks (University of California at San Francisco)

Eneida A Mendonça (University of Wisconsin - Madison Institute for Clinical and Translational Research)

Tom Mish (University of Wisconsin - Madison Institute for Clinical and Translational Research)

Ketty Mobed (University of California at San Francisco)

Shawn Murphy (Partners Healthcare Research Computing / Harvard Medical School)

Mark Musen (Stanford Center for Biomedical Informatics Research / NCBO)

Jihad Obeid (Medical University of South Carolina)

John Mark Ockerbloom (University of Pennsylvania Libraries)

M. Theresa Perry (RCMI TRN Data and Technology Coordinating Center, Jacksonville State University)

Lori C. Phillips (Partners, Harvard)

Taylor Pressler (The Ohio State University Center for Clinical and Translational Science)

Mark Ressler (University at Buffalo)

Blake Roessler (Research Innovation, University of Michigan / Michigan Institute for Clinical and Health Research)

Alan Ruttenberg (University at Buffalo Clinical and Translational Data Exchange)

Jody Sachs (National Center for Advancing Translational Sciences, NIH)

Michael Sayre (National Institute on Minority Health and Health Disparities, NIH)

Richard Scheuermann (North and Central Texas Clinical and Translational Science Initiative / University of Texas Southwestern Medical Center at Dallas)

Titus Karl Ludwig Schleyer (University of Pittsburgh Center for Dental Informatics)

Anne Seymour (Biomedical Library, University of Pennsylvania)

Nigam Shah (Stanford Center for Biomedical Informatics Research / NCBO)

Amitava Shee (University of Michigan / Michigan Institute for Clinical & Health Research)

Barry Smith (University at Buffalo / NCBO)

Dagobert Soergel (University at Buffalo)

Alisa Surkis (New York University School of Medicine)

Umberto Tachinardi (University of Wisconsin - Madison Institute for Clinical and Translational Research)

Jessica Tenenbaum (Duke Translational Medicine Institute)

Carlo Torniai (Oregon Health & Science University / CTSA Connect)

Anita Walden (Duke University)

Trish Whetzel (Stanford University / NCBO)

Rob Wynden (University of California at San Francisco)

Debbie Yoshihara (University of Wisconsin, Madison CTSA)

Xin Zheng (Yeshiva University / Einstein CTSA)