biomedical and health informatics lecture series peter tarczy-hornoch md head and professor,...
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Biomedical and Health InformaticsLecture Series
Peter Tarczy-Hornoch MDHead and Professor,
Division of Biomedical and Health InformaticsUniversity of Washington
October 2, 2007
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: Broader UW & Seattle community interested in BHI BHI faculty and students
History: 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: http://courses.washington.edu/mebi590/ [email protected]
Key points for those taking for credit Need to sign in each lecture to get credit CR/NC course Must attend 9 of 10 lectures for credit
Informatics and theNew Northwest Institute of
Translational Health Sciences
Peter Tarczy-Hornoch MDDirector, Biomedical Informatics Core
Northwest Institute of Translational Health Sciences
Head and Professor, Division of Biomedical and Health InformaticsProfessor, 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)
http://nihroadmap.nih.gov/ Chart a roadmap for medical research in 21st c.
NIH Leadership 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 Building Blocks, Biological Pathways, and Networks Molecular Libraries & Molecular Imaging Structural Biology Bioinformatics and Computational Biology (BISTI/NCBC) Nanomedicine
Research Teams of the Future High-Risk Research Interdisciplinary Research Public-Private Partnerships
Re-engineering the Clinical Research Enterprise 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 RoadmapClinical Translational Science Awards
Initial request for applications October 2005 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 multi- and 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 Modified from the NCI translational research working
group (2006) UW: human subjects, specimens or plans CTSA: From Bench to Bedside to Community
NIH RoadmapClinical 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
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)
Planning grant: 2006-7 Full Center grant: 2007-12 funded $62M
NW ITHS will provide an “academic home” and integrated resources to:
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 - 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 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:
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
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 5: Develop & maintain ITHS administrative databases & Web interfaces
Aim 1
Aim 2
Aim 3
Aim 4
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:
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:
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 Goals:
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
Goals: 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
Peter Tarczy-Hornoch MD, PI Dan Suciu PhD, PI Alon Halevy PhD, Past PI 6 collaborating faculty
Jim Brinkley, Chris Carlson, Eugene Kolker, Peter Myler, 4 programmers
Ron Shaker, Todd Detwiler 13 students (over time)
Eithon Cadag, Brent Louie, Terry Shen, Kelan Wang
Motivation for Data Integration
Knowledge
Data
Information
Discovery(understanding)
Genomics
Proteomics
Literature
Clinical Data
ExperimentalData
Pathways
Others…
Adapted from Chung and Wooley. 2003Slide K. Wang, 2005
The Growth of Biologic Databases
(Nucleic Acids Research, Database Issues 2000-2006) Slide E Cadag, 2006
0
100
200
300
400
500
600
700
800
900
2000 2001 2002 2003 2004 2005 2006
Year
Data
bases
BioMediator System Federated, general purpose, modular, decoupled NIH NHGRI/NLM funded 2000-2007 www.biomediator.org
PfamInterface
CDD
ProSite
Interface
Interface
Translation
Common data model
Query
Query
Query`
Query`
Query`
Query``
Query``
Query``
BioMediator Use Case: Annotation
Pfam
PubMed Entrez
GO
PROSITE
CDD
COGs
BLOCKS
PSORTLocal
databases
Localalgorithms
BLAST
Human analysis andcuration
Slide E Cadag, 2006
Inference to Emulate Human AnnotatorRule-base
IF DomainHit e-value > 10e-15
THEN remove
IF DatabaseHit Name is similar to other DatabaseHit Names
THEN increase evidence
...
Working memory
Pfam.DomainHit e-value: 10e-10 name: neurotransmitterProSite.DomainHit e-value: 10e-20 name: neurotrans.BLAST.DatabaseHit e-value: 10e-10 name: nic. acetylcholineBLAST.DatabaseHit e-value: 10e-20 name: acetylcholine rec.
...
evidence for
acetylcholine increased
Slide E. Cadag, 2006
Evaluation Scoring System
Dimensions of granularity and utilityScore 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
+1 Automated annotation is more specific than actual
Automated annotation is more useful than actual
Slide E. Cadag, 2006
Automated Score Granularity,% (n)
Utility,% (n)
Incorrect or useless 3.0% (1) 0% (0)
Less granular or useful 20.6% (7) 5.8% (2)
Same as actual 52.9% (18) 73.6% (25)
More granular or useful 23.5% (8) 20.6% (7)
Total 100% (34) 100% (34)
Granularity average (selected annotations): -0.029Utility average (selected annotations): 0.147
Scores for Automated Annotations
Slide E. Cadag, 2006
Data Source Measures: Ps
Concept 1 Concept 2
Concept 2Concept 1
Source 1 Source 2
Source 3 Source 4
Ps: users belief in a concept from a particular source
Slide B. Louie, 2007
Data Source Measures: Qs
Concept 1 Concept 2
Concept 2Concept 1
Source 1 Source 2
Source 3 Source 4
Qs: users belief in the interconnections (relationship) between two sources
relationship
relationship
relationship
Slide B. Louie, 2007
Data Record Measures: Pr
Concept 1 Concept 2
Source 1 Source 2
Pr: measure of belief in a particular data record
Record 2Record 1
Slide B. Louie, 2007
Data Record Measures: Qr
Concept 1 Concept 2
Source 1 Source 2
Qr: measure of belief in a particular link between data records
Record 2Record 1link
Slide B. Louie, 2007
Result Graph with Uncertainty Measures
Ps: 1.0Pr: 0.8
Ps: 0.8Pr: 0.5
Ps: 0.7Pr: 0.3 Qs:
0.8Qr: 0.3
Qs: 0.8
Qr: 0.9
Slide B. Louie, 2007
Network Reliability TheorySUII (U2) Score = probability that
a node is reachable fromthe start (seed) node.
Computing U2 score is #P. Approximation algorithms exist (Karger 2001), but are impractical.
Psn1* Prn1
Psn1* Prn1
Psn1* Prn1
Psn1* Prn1
Psn1* Prn1
Qse1* Qre1
Qse1* Qre1
Qse1* Qre1
Qse1* Qre1Qse1* Qre1
Qse1* Qre1
Qse1* Qre1
Slide B. Louie, 2007
Ps: 1.0Pr: 0.8
U2: 0.80
Ps: 0.8Pr: 0.5
U2: 0.40
Ps: 0.7Pr: 0.3
U2: 0.21
Qs: 0.8Qr: 0.3
U2: 0.24
Qs: 0.8Qr: 0.9
U2: 0.72
Slide B. Louie, 2007
Result Graph with Uncertainty Scores
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 5: Develop & maintain ITHS administrative databases & Web interfaces
Aim 1
Aim 2
Aim 3
Aim 4
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