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Biomedical and Health Informatics Lecture Series

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

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  1. October 2, 2007 Biomedical and Health InformaticsLecture Series Peter Tarczy-Hornoch MD Head and Professor, Division of Biomedical and Health Informatics University of Washington

  2. 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

  3. 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/ • pth@u.washington.edu • 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

  4. 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

  5. Outline • Clinical Translational Science Awards • Northwest Institute of Translational Health Sciences • Biomedical Informatics Core of NW ITHS • Data Integration • Summary

  6. 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)

  7. 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)

  8. 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.”

  9. 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

  10. 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

  11. National CTSA Awards 2006 & 2007

  12. 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

  13. Outline • Clinical Translational Science Awards • Northwest Institute of Translational Health Sciences • Biomedical Informatics Core of NW ITHS • Data Integration • Summary

  14. 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

  15. NW ITHS – “Collaboratory” Model

  16. 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

  17. 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

  18. Outline • Clinical Translational Science Awards • Northwest Institute of Translational Health Sciences • Biomedical Informatics Core of NW ITHS • Data Integration • Summary

  19. 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.

  20. 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

  21. ITHS Biomedical Informatics Core Aim 1 Aim 3 Aim 2 Aim 4 Aim 5: Develop & maintain ITHS administrative databases & Web interfaces

  22. 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

  23. 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

  24. 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

  25. 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

  26. 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

  27. 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

  28. 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

  29. 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

  30. Outline • Clinical Translational Science Awards • Northwest Institute of Translational Health Sciences • Biomedical Informatics Core of NW ITHS • Data Integration • Summary

  31. 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

  32. 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

  33. The Growth of BiologicDatabases (Nucleic Acids Research, Database Issues 2000-2006) Slide E Cadag, 2006

  34. 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

  35. BioMediator Use Case: Annotation PubMed Entrez PROSITE COGs GO BLAST Human analysis andcuration Localdatabases PSORT Pfam Local algorithms CDD BLOCKS Slide E Cadag, 2006

  36. Finding Needle in Haystack: Inference Complete Result Set Relevant Subset

  37. 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

  38. Evaluation Scoring System Dimensions of granularity and utility Slide E. Cadag, 2006

  39. Scores for Automated Annotations Granularity average (selected annotations): -0.029Utility average (selected annotations): 0.147 Slide E. Cadag, 2006

  40. Finding Needle in Haystack: UncertaintyNSF IIS funded 2005-2009 Complete Result Set Relevant Subset

  41. 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

  42. 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

  43. 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

  44. 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

  45. 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

  46. 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

  47. 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

  48. 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%

  49. NW ITHS and Data Integration Aim 1 Aim 3 Aim 2 Aim 4 Aim 5: Develop & maintain ITHS administrative databases & Web interfaces

  50. Outline • Clinical Translational Science Awards • Northwest Institute of Translational Health Sciences • Biomedical Informatics Core of NW ITHS • Data Integration • Summary

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