Download - Biomedical and Health Informatics Lecture Series

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

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



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 - 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


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


Outline



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



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

Finding Needle in Haystack: Inference

Complete Result Set

Relevant Subset

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


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


Finding Needle in Haystack: UncertaintyNSF IIS funded 2005-2009

Complete Result Set

Relevant Subset

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


Data Record Measures: Pr

Concept 1 Concept 2

Source 1 Source 2

Pr: measure of belief in a particular data record

Record 2Record 1


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


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


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


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


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



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

Download - Biomedical and Health Informatics Lecture Series

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