October 2, 2007
Biomedical and Health Informatics
Lecture Series
Peter Tarczy-Hornoch MD
Head and Professor,
Division of Biomedical and Health Informatics
University of Washington
Biomedical and Health Informatics
Lecture Series
 Focus: current topics and developments in informatics
 Presenters: faculty, students, researchers and developers
from UW, other academic institutions, government, and
industry (locally and nationally)
 Intended audience:
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Broader UW & Seattle community interested in BHI
BHI faculty and students
 History:
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Early 1990’s: initiated as part of IAIMS (MEDED 590)
2003-2006: temporarily changed to closed journal club format
Fall 2006: return to public lecture series format
Fall 2007: 10th year of Division of Biomedical & Health Informatics
MEBI 590 & BHI Lecture Series
 Biomedical and Health Informatics (BHI) Lecture
series available for credit as MEBI 590
 Details & upcoming lectures available at:
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http://courses.washington.edu/mebi590/
pth@u.washington.edu
 Key points for those taking for credit
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Need to sign in each lecture to get credit
CR/NC course
Must attend 9 of 10 lectures for credit
Informatics and the
New Northwest Institute of
Translational Health Sciences
Peter Tarczy-Hornoch MD
Director, Biomedical Informatics Core
Northwest Institute of Translational Health Sciences
Head and Professor, Division of Biomedical and Health Informatics
Professor, Division of Neonatology
bhi.washington.edu
Outline
 Clinical Translational Science Awards
 Northwest Institute of
Translational Health Sciences
 Biomedical Informatics Core of NW ITHS
 Data Integration
 Summary
NIH Roadmap - Process
 Initiated in 2002 by NIH Director (Zerhouni)
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http://nihroadmap.nih.gov/
 Chart a roadmap for medical research in 21st c.
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NIH Leadership
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What are today’s scientific challenges?
What are the roadblocks to progress?
What do we need to do to overcome roadblocks?
What can’t be accomplished by any single Institute – but is the
responsibility of NIH as a whole
Working Groups
Implementation Groups
 Implementation Groups => RFAs
 Summer/Fall 2006: New initiatives (Roadmap 1.5)
NIH Roadmap – Themes
 New Pathways to Discovery
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Building Blocks, Biological Pathways, and Networks
Molecular Libraries & Molecular Imaging
Structural Biology
Bioinformatics and Computational Biology (BISTI/NCBC)
Nanomedicine
 Research Teams of the Future
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High-Risk Research
Interdisciplinary Research
Public-Private Partnerships
 Re-engineering the Clinical Research Enterprise
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Clinical Research Networks/NECTAR
Clinical Research Policy Analysis and Coordination
Clinical Research Workforce Training
Dynamic Assessment of Patient-Reported Chronic Disease Outcomes
Translational Research (Clinical Translational Science Awards)
NIH Roadmap
Clinical Translational Science Awards
 Initial request for applications October 2005
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Current RFA: RFA-RM-07-007
CTSA planning grants (one year), implementation grants (five years)
 “The purpose of this initiative is to assist institutions to create
a uniquely transformative, novel, and integrative academic
home for Clinical and Translational Science that has the
resources to train and advance a cadre of well-trained multiand inter-disciplinary investigators and research teams with
access to innovative research tools and information
technologies to promote the application of new knowledge
and techniques to patient care.”
Definition of Translational Research
 “Translational research transforms scientific
discoveries arising from laboratory, clinical or
population studies into clinical or population-based
applications to improve health by reducing disease
incidence, morbidity and mortality
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Modified from the NCI translational research working
group (2006)
 UW: human subjects, specimens or plans
 CTSA: From Bench to Bedside to Community
NIH Roadmap
Clinical Translational Science Awards
 Integrate existing Clinical Research Centers (CRCs) with
existing clinical/translational science training grants (K12,
K30, T32) and expand capabilities through new cores (e.g.
Biomedical Informatics, Evaluation, Novel Technologies, etc.)
 Establish regional and national consortia with the aim of
transforming how clinical and translational research is
conducted, and ultimately enabling researchers to provide
new treatments more efficiently and quickly to patients
 When fully implemented in 2012, the initiative is expected to
provide a total of about $500 million annually to 60 academic
health centers in the US
National CTSA Awards 2006 & 2007
CTSA Full Center Awards
2006
 Columbia University Health Sciences
 Duke University
 Mayo Clinic College of Medicine
 Oregon Health & Science University
 Rockefeller University
 University of California, Davis
 University of California, San
Francisco
 University of Pennsylvania
 University of Pittsburgh
 University of Rochester
 University of Texas Health Science
Center at Houston
 Yale University
2007
 Case Western Reserve University
 Emory University
 Johns Hopkins
 University of Chicago
 University of Iowa
 University of Michigan
 University of Texas Southwestern
Medical Center
 University of Washington
 University of Wisconsin
 Vanderbilt University
 Washington University
 Weill Cornell Medical College
Outline
 Clinical Translational Science Awards
 Northwest Institute of
Translational Health Sciences
 Biomedical Informatics Core of NW ITHS
 Data Integration
 Summary
Institute of Translational Health Sciences
 Northwest ITHS is the name for the regional inter-disciplinary
consortium funded through the NIH-NCRR Clinical
Translational Science Award (CTSA)
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Planning grant: 2006-7
Full Center grant: 2007-12 funded $62M
 NW ITHS will provide an “academic home” and integrated
resources to:
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Advance clinical and translational science;
Create and nurture a cadre of well-trained clinical investigators;
Speed translation of discoveries into clinical practice
Foster interactions between the university, non-profit, and business
research communities
Create an incubator for novel ideas and collaborations that cross
disciplines
Institute of Translational Health Sciences
NW ITHS – “Collaboratory” Model
NW ITHS - Partners
 Founding Members of the NW ITHS and Key Collaborators
 University of Washington
 Children’s Hospital and Regional Medical Center
 Fred Hutchinson Cancer Research Center
 Group Health Cooperative Center for Health Studies
 Benaroya Research Institute
 PATH
 Six proposed American Indian and Alaska Native Network Sites
 6 Health Sciences School, 12 sites, 67 key scientific personnel, more than
150 centers
 Drs. Nora Disis (UW), Bonnie Ramsey (CHRMC), Mac Cheever
(FHCRC/SCCA) co-leaders
Institute of Translational Health Sciences
Eleven ITHS Cores
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Administrative
Novel clinical and translational methodologies
Pilot and collaborative translational and clinical studies
Biomedical informatics
Study design and biostatistics
Regulatory knowledge, support and research ethics
Participant clinical interactions resources (CRC+)
Community engagement
Translational technologies and resources
Research education, training and career development
Tracking and evaluation
Institute of Translational Health Sciences
Outline
 Clinical Translational Science Awards
 Northwest Institute of
Translational Health Sciences
 Biomedical Informatics Core of NW ITHS
 Data Integration
 Summary
CTSA RFA & Biomedical Informatics
 Biomedical Informatics is the cornerstone of communication within
(CTSAs) and with all collaborating organizations
 Applicants should describe:
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support provided for operations, administration, research and
clinical/translational research activities
plan to establish communication with external organizations relevant to their
mission
the process by which standards and other mechanisms will be developed and
used to maximize interoperability between internal systems and systems in
outside organizations
assessment of informatics performance across the CTSA programs and with
external partners
inter- and intra-organizational sharing of data, technology and best practices
 Biomedical Informatics is expected to be the subject of an overall NIH
CSTA Informatics Steering Committee that ensures interoperability
between the CTSA institutions and with their external partners.
Biomedical Informatics Core Team
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Peter Tarczy-Hornoch MD, Core Director
Jim Brinkley MD PhD, Core Co-Director
Nick Anderson PhD, Core Deputy Director
Bill Lober MD
Jim LoGerfo MD MPH
Dan Suciu PhD
Dan Ach (GCRC Informatics Lead)
To be hired: ~14 professional staff and 3 RA slots
ITHS Biomedical Informatics Core
Aim 1
Aim 3
Aim 2
Aim 4
Aim 5: Develop & maintain ITHS administrative databases & Web interfaces
Aim 1: Provide access to electronic
health data at ITHS institutions
 Inventory and model recurring common queries
 Develop new interfaces to electronic health data from partner
institutions
 Provide ITHS researchers access to electronic health data
from partner institutions via a new common web interface
 Pilot a Virtual Data Warehouse (VDW) across the ITHS
partner institutes building on the common web interface
 Extend the pilot VDW to include clinics in the WWAMI region
Access to electronic health record data
 Existing resources: MIND Access Project (UW),
Cerner Research Query System (CHRMC), Clinical
Data Repository (FHCRC), Research-O-Matic (CHS)
 Gaps: no convenient access, repository data limited
 Goals:
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Simplify appropriate access to existing data
Extend appropriate access to existing data
Extend sources of electronic health record data
 Note: research still needed to solve Aim 1-4 gaps
Aim 2: Support access to study data
management tools for translational research
 Provide consultation to ITHS researchers regarding choosing
and implementing study management tools
 Continue to develop and enhance existing ITHS data
management tools
 Maintain and augment an inventory of data management
tools
 Develop interfaces to most commonly use data management
tools
 Perform a feasibility study of the establishment of a Data
coordinating center
Access to study data management tools
 Existing resources: GCRC Study Data Management
(UW/CHRMC), Seedpod/Celo (UW), CF TDN
(CHRMC), Clinical Informatics Shared Resource
(FHCRC), multiple tools elsewhere
 Gaps: ease of use, limited features, not integrated
 Goals:
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Move local systems from prototype to production
Develop centralized resources for currently used case
report forms/study data management tools
Extend centralized repository to include other CTSA tools
Aim 3: Interface to biological study data
from scientific instrumentation cores
 Provide ITHS researchers access to data from ITHS
scientific instrumentation cores
 Prioritize list of other scientific instrumentation cores
suitable to access
 Develop protocols and interfaces to new ITHS
Human Genomics and Coordinated Tissue Bank
core
Access to instrumentation cores data
 Existing resources: large number of scientific
instrumentation cores across consortium sites,
generalizing interfaces via caBIG & SCHARP
collaboration with Labkey Software (FHCRC)
 Gap: data not integrated with clinical/study data
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Build reusable interfaces to key scientific instrumentation
Ensure compatibility with Aim 4 and national standards
Aim 4: Integrate access across these
three data sources
 Provide ad-hoc integration of aims 1-3 to ITHS
researchers via ITHS BMI personnel
 Develop a data integration model for ITHS BMI by
adapting existing tools
 Implement, test and refine prototype ITHS BMI Data
Integration System
 Deploy and continue to refine the ITHS BMI data
integration system
Integrate access across these resources
 Existing resources: BioMediator (UW), XBrain (UW),
CNICS, NA-ACCORD (UW), MIND/MAP (UW),
Clinical Data Repository (FHCRC), caBIG (FHCRC),
SCHARP (FHCRC), Virtual Data Warehouse (CHS)
 Gaps: no system integrates sources from Aim 1-3,
no system across consortium members
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Adapt and evolve existing local systems to meet needs
Continue to assess commercial systems
Adopt interoperable approaches across CTSA sites
Outline
 Clinical Translational Science Awards
 Northwest Institute of
Translational Health Sciences
 Biomedical Informatics Core of NW ITHS
 Data Integration
 Summary
UW Biomedical Data Integration and
Analysis Research Group
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Peter Tarczy-Hornoch MD, PI
Dan Suciu PhD, PI
Alon Halevy PhD, Past PI
6 collaborating faculty
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Jim Brinkley, Chris Carlson, Eugene Kolker, Peter Myler,
 4 programmers
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Ron Shaker, Todd Detwiler
 13 students (over time)
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Eithon Cadag, Brent Louie, Terry Shen, Kelan Wang
Motivation for Data Integration
Genomics
Data
Literature
Clinical Data
Proteomics
Information
Pathways
Knowledge
Discovery
(understanding)
Experimental
Data
Others…
Adapted from Chung and Wooley. 2003
Slide K. Wang, 2005
The Growth of Biologic Databases
900
800
700
Databases
600
500
400
300
200
100
0
2000
2001
2002
2003
2004
2005
2006
Year
(Nucleic Acids Research, Database Issues 2000-2006) Slide E Cadag, 2006
BioMediator System
 Federated, general purpose, modular, decoupled
 NIH NHGRI/NLM funded 2000-2007
 www.biomediator.org
Interface
Pfam
Query`
Query``
Query
Translation
Query
Query`
Interface
Query``
ProSite
Interface
Query`
Common data model
CDD
Query``
BioMediator Use Case: Annotation
PubMed
Entrez
PROSITE
COGs
GO
BLAST
Human analysis and
curation
Local
databases
PSORT
Pfam
CDD
BLOCKS
Local
algorithms
Slide E Cadag, 2006
Finding Needle in Haystack: Inference
Complete
Result Set
Relevant
Subset
Inference to Emulate Human Annotator
Working memory
Pfam.DomainHit
IF DomainHit e-value >
e-value: 10e-10
10e-15
name: neurotransmitter
ProSite.DomainHit
THEN remove
e-value: 10e-20
name: neurotrans.
IF DatabaseHit Name is
BLAST.DatabaseHit
similar to other
e-value: 10e-10
DatabaseHit Names
name: nic.
acetylcholine
THEN increase evidence
BLAST.DatabaseHit
e-value: 10e-20
evidence for
name: acetylcholine
acetylcholine increased
rec.
...
Rule-base
...
Slide E. Cadag, 2006
Evaluation Scoring System
Dimensions of granularity and utility
Score
Granularity Meaning
Utility Meaning
-2
Automated annotation is
incorrect
Phrasing or representation of
automated annotation is not useful
for functional annotation
-1
Automated annotation is less
specific than actual
Automated annotation is less useful
than actual
0
Automated annotation is
indistinguishable from actual
Automated annotation is as useful as
actual
Automated annotation is
more specific than actual
Automated annotation is more useful
than actual
+1
Slide E. Cadag, 2006
Scores for Automated Annotations
Automated Score
Incorrect or useless
Less granular or useful
Same as actual
More granular or useful
Total
Granularity,
% (n)
Utility,
% (n)
3.0% (1)
0% (0)
20.6% (7)
5.8% (2)
52.9% (18)
73.6% (25)
23.5% (8)
20.6% (7)
100% (34)
100% (34)
Granularity average (selected annotations): -0.029
Utility average (selected annotations):
0.147
Slide E. Cadag, 2006
Finding Needle in Haystack: Uncertainty
NSF IIS funded 2005-2009
Complete
Result Set
Relevant
Subset
Data Source Measures: Ps
Source 1
Source 2
Concept 1
Concept 2
Source 3
Source 4
Concept 1
Concept 2
Ps: users belief in a concept from a particular source
Slide B. Louie, 2007
Data Source Measures: Qs
Source 2
Source 1
relationship
Concept 1
Concept 2
Source 3
Source 4
Concept 1
Concept 2
relationship
Qs: users belief in the interconnections
(relationship) between two sources
Slide B. Louie, 2007
Data Record Measures: Pr
Source 1
Source 2
Concept 1
Concept 2
Record 1
Record 2
Pr: measure of belief in a particular data record
Slide B. Louie, 2007
Data Record Measures: Qr
Source 1
Source 2
Concept 1
Concept 2
link
Record 1
Record 2
Qr: measure of belief in a particular link
between data records
Slide B. Louie, 2007
Result Graph with Uncertainty Measures
Qs:
0.8
Qr: 0.9
Ps:
0.7
Pr: 0.3
Ps:
1.0
Pr: 0.8
Ps:
0.8
Pr: 0.5
Qs:
0.8
Qr: 0.3
Slide B. Louie, 2007
Network Reliability Theory
Qse1* Qre1
UII (U2) Score = probability that
a node is reachable from
the start (seed) node.
S
Psn1* Prn1
Qse1* Qre1
Qse1* Qre1
Psn1* Prn1
Psn1* Prn1
Qse1* Qre1
Qse1* Qre1
Qse1* Qre1
Psn1* Prn1
Psn1* Prn1
Computing U2 score is #P.
Approximation algorithms
exist (Karger 2001), but are
impractical.
Qse1* Qre1
Slide B. Louie, 2007
Result Graph with Uncertainty Scores
Qs: 0.8
Qr: 0.9
U2: 0.72
Ps: 0.7
Pr: 0.3
U2: 0.21
Ps: 1.0
Pr: 0.8
U2: 0.80
Ps: 0.8
Pr: 0.5
U2: 0.40
Qs: 0.8
Qr: 0.3
U2: 0.24
Slide B. Louie, 2007
BioMediator & Uncertainty: Evaluation
 Preliminary evaluation
 Gold standard: COG functional categorization
 Comparison: BioMediator + Uncertainty
 Agreement with actual: 94.4%
 After increasing number of simulations to
estimate UII scores: 100%
NW ITHS and Data Integration
Aim 1
Aim 3
Aim 2
Aim 4
Aim 5: Develop & maintain ITHS administrative databases & Web interfaces
Outline
 Clinical Translational Science Awards
 Northwest Institute of
Translational Health Sciences
 Biomedical Informatics Core of NW ITHS
 Data Integration
 Summary
Summary/Questions
 CTSAs are seen as a key part of the NIH Roadmap
“Re-engineering the clinical research enterprise”
 Biomedical informatics (BMI) cores are seen as key
nationally as well as locally for NW ITHS
 The BMI core is focused on addressing identified
gaps through both research and tool development
 An important foundational element to the BMI core is
data integration