Biomedical and health informatics lecture series
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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

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Biomedical and health informatics lecture series

October 2, 2007

Biomedical and Health InformaticsLecture Series

Peter Tarczy-Hornoch MD

Head and Professor,

Division of Biomedical and Health Informatics

University of Washington


Biomedical and health informatics lecture series1

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

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 the new northwest institute of translational health sciences

Informatics and theNew 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

Outline

  • Clinical Translational Science Awards

  • Northwest Institute of Translational Health Sciences

  • Biomedical Informatics Core of NW ITHS

  • Data Integration

  • Summary


Nih roadmap process

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

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 roadmap 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

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 roadmap clinical translational science awards1

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


National ctsa awards 2006 2007

National CTSA Awards 2006 & 2007


Ctsa full center awards

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


Outline1

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

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 collaboratory model

NW ITHS – “Collaboratory” Model


Nw iths partners

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

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


Outline2

Outline

  • Clinical Translational Science Awards

  • Northwest Institute of Translational Health Sciences

  • Biomedical Informatics Core of NW ITHS

  • Data Integration

  • Summary


Ctsa rfa biomedical informatics

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

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

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

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

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

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

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

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

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

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

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


Outline3

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

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

Motivation for Data Integration

Literature

Genomics

Data

Clinical Data

Proteomics

Information

Experimental

Data

Pathways

Others…

Knowledge

Discovery

(understanding)

Adapted from Chung and Wooley. 2003

Slide K. Wang, 2005


The growth of biologic databases

The Growth of BiologicDatabases

(Nucleic Acids Research, Database Issues 2000-2006) Slide E Cadag, 2006


Biomediator system

BioMediator System

Pfam

Query

Translation

Interface

Query`

Query``

CDD

Interface

Query

Query`

Query``

ProSite

Interface

Query`

Query``

Common data model

  • Federated, general purpose, modular, decoupled

  • NIH NHGRI/NLM funded 2000-2007

  • www.biomediator.org


Biomediator use case annotation

BioMediator Use Case: Annotation

PubMed

Entrez

PROSITE

COGs

GO

BLAST

Human analysis andcuration

Localdatabases

PSORT

Pfam

Local

algorithms

CDD

BLOCKS

Slide E Cadag, 2006


Finding needle in haystack inference

Finding Needle in Haystack: Inference

Complete Result Set

Relevant Subset


Inference to emulate human annotator

Inference to Emulate Human Annotator

Working memory

Rule-base

Pfam.DomainHite-value: 10e-10name: neurotransmitter

IFDomainHit e-value > 10e-15

THEN remove

ProSite.DomainHite-value: 10e-20name: neurotrans.

IFDatabaseHit Name is similar to other DatabaseHit Names

THEN increase evidence

BLAST.DatabaseHite-value: 10e-10name: nic. acetylcholine

BLAST.DatabaseHite-value: 10e-20name: acetylcholine rec.

evidence for

acetylcholine increased

...

...

Slide E. Cadag, 2006


Evaluation scoring system

Evaluation Scoring System

Dimensions of granularity and utility

Slide E. Cadag, 2006


Scores for automated annotations

Scores for Automated Annotations

Granularity average (selected annotations): -0.029Utility average (selected annotations): 0.147

Slide E. Cadag, 2006


Finding needle in haystack uncertainty nsf iis funded 2005 2009

Finding Needle in Haystack: UncertaintyNSF IIS funded 2005-2009

Complete Result Set

Relevant Subset


Data source measures ps

Data Source Measures: Ps

Source 2

Source 1

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

Data Source Measures: Qs

Source 2

Source 1

relationship

Concept 1

Concept 2

relationship

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

Data Record Measures: Pr

Source 2

Source 1

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

Data Record Measures: Qr

Source 2

Source 1

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

Result Graph with Uncertainty Measures

Qs: 0.8

Qr: 0.9

Ps: 1.0

Pr: 0.8

Ps: 0.8

Pr: 0.5

Ps: 0.7

Pr: 0.3

Qs: 0.8

Qr: 0.3

Slide B. Louie, 2007


Network reliability theory

Network Reliability Theory

Qse1* Qre1

S

UII (U2) Score = probability that

a node is reachable from

the start (seed) node.

Psn1* Prn1

Qse1* Qre1

Qse1* Qre1

Psn1* Prn1

Psn1* Prn1

Qse1* Qre1

Qse1* Qre1

Qse1* Qre1

Computing U2 score is #P. Approximation algorithms exist (Karger 2001), but are impractical.

Psn1* Prn1

Psn1* Prn1

Qse1* Qre1

Slide B. Louie, 2007


Biomedical and health informatics lecture series

Result Graph with Uncertainty Scores

Qs: 0.8

Qr: 0.9

U2: 0.72

Ps: 1.0

Pr: 0.8

U2: 0.80

Ps: 0.8

Pr: 0.5

U2: 0.40

Ps: 0.7

Pr: 0.3

U2: 0.21

Qs: 0.8

Qr: 0.3

U2: 0.24

Slide B. Louie, 2007


Biomediator uncertainty evaluation

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

NW ITHS and Data Integration

Aim 1

Aim 3

Aim 2

Aim 4

Aim 5: Develop & maintain ITHS administrative databases & Web interfaces


Outline4

Outline

  • Clinical Translational Science Awards

  • Northwest Institute of Translational Health Sciences

  • Biomedical Informatics Core of NW ITHS

  • Data Integration

  • Summary


Summary questions

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


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