Biomedical Science in the Net of the 21st Century: A Tale of Two Systems for Global Infrastructure

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Biomedical Science in the Net of the 21st
CenturyA Tale of Two Systems for Global
Infrastructure
Bruce R. SchatzDepartment of Medical Information
Science and Institute for Genomic
Biology University of Illinois at
Urbana-Champaign schatz_at_uiuc.edu,
www.canis.uiuc.edu
School of Information Sciences and Department of
Biomedical Informatics University of Pittsburgh,
February 8, 2007
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The Grand Vision
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Computer Science and Infrastructure

• Transparent Federation across Sources
• Generic Protocols for Global Infrastructure
• Ultimate Goal is cyberspace visions of
• being one with all the worlds knowledge

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A Tale of Two Systems

• Charles Dickens on Daily Life
• It was the best of times, it was the worst
  of times

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THE THIRD WAVE OF NET EVOLUTION
CONCEPTS
OBJECTS
PACKETS
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Linguistics Levels and Universal Units

• 1985 Syntax Files (wholes)
• 1995 Structure Objects (parts)
• 2005 Semantics Concepts (meaning)
• 2015 Pragmatics Features (reality)

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A Tale of Two Conferences

• Information Science
• Y. Chung, Q. He, K. Powell and B. Schatz (1999)
  Semantic Indexing for a Complete Subject
  Discipline, 4th Int ACM Conf on Digital
  Libraries, Berkeley CA, Aug, 39-48.
• Biomedical Informatics
• N. Bennett, Q. He, K. Powell, and B. Schatz
  (1999) Extracting Noun Phrases for All of
  MEDLINE, AMIA '99 (American Medical Informatics
  Assoc) Annual Conf, Washington DC, Nov,
  671-675.  
• won Best Theoretical Paper award

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Navigation in MEDSPACE

• For a patient with Rheumatoid Arthritis
• Find a drug that reduces the pain (analgesic)
• but does not cause stomach (gastrointestinal)
  bleeding

Choose Domain
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Concept Navigation
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Retrieve Document
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Complex Systems for Health 1

• Understanding Functional Analysis
• Emergent Properties of Human Health
• can only be Discovered via the
• Emergent Properties of Distributed Systems
• The Interspace is the next generation
• of the Net
• Where Concept Navigation across Distributed
  Communities is routine

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What are Analysis Environments

• Functional Analysis
• Find the underlying Mechanisms
• Of Genes, Behaviors, Diseases
• Comparative Analysis
• Top-down data mining (vs Bottom-up)
• Multiple Sources especially literature

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Building Analysis Environments

• Manual by Humans
• Interaction user navigation
• Classification collection indexing
• Automatic by Computers
• Federation search bridges
• Integration results links

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Informational Science

• Computational Science is widely accepted as
• The Third Branch of Science
• (beyond Experimental and Theoretical)
• Genes are Computed, Proteins are Computed,
• Sequence equivalences are Computed.
• Informational Science is coming to be accepted as
• The Fourth Branch of Science
• Based on Information Science technologies for
• Functional Analysis across Information Sources

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Informatics From Bases to Spaces

• data Bases support genome data
• e.g. FlyBase has sequences and maps
• Genes annotated by GO and linked to literature
• e.g. BeeBase has computed annotations
• Protein homologies for similar Genes via GO
• information Spaces support biomedical literature
• e.g. BeeSpace uses automatically generated
• conceptual relationships to navigate functions

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Conceptual Navigation in BeeSpace
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Towards the Interspace

• from Objects to Concepts
• from Syntax to Semantics
• Infrastructure is Interaction with Abstraction

Internet is packet transmission across
computers Interspace is concept navigation
across repositories
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LEVELS OF INDEXES
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XSpace Information Sources

• Organize Genome Databases (XBase)
• Compute Gene Descriptions from Model Organisms
• Partition Scientific Literature for Organism X
• Compute XSpace using Semantic Indexing
• Boost the Functional Analysis from Special
  Sources
• Collecting Useful Data about Natural Histories
• e.g. CowSpace Leverage in AIPL Databases

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Towards the Interspace

• The Analysis Environment technology is
  GENERAL! BirdSpace? BeeSpace?
• PigSpace? CowSpace?
• BehaviorSpace? BrainSpace?
• BioSpace
• Interspace

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A Tale of Two Conferences

• Information Science
• B. Chee and B. Schatz (2007) Document
  Clustering using Small Worlds Communities,
  Joint ACM/IEEE Conf on Digital Libraries (JCDL
  07), Vancouver BC, Jun, 10pp, submitted.
• Biomedical Informatics
• D. Maduram and B. Schatz (2007) Monitoring
  Quality-of-Life via Full-Spectrum
  Questionnaires, American Medical Informatics
  Assoc Annual Conf (AMIA 07), Chicago IL, Nov,
  5pp, submitted.

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Small Worlds Networks

• Community Structure enables Dynamic Clustering
  with Large Vectors

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THE FOURTH WAVE OF NET EVOLUTION
FEATURES
CONCEPTS
OBJECTS
PACKETS
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Fourth Wave Pragmatics Federation

• Beyond Words and Concepts to Reality
• Feature Vectors describing Situation
• Each Individual has Vector (lt Community)
• Discrete Samples into Continuous Monitors

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Feature Vectors

• from Concepts to Features
• from Semantics to Pragmatics
• Infrastructure is Interaction with Abstraction

Interspace is concept navigation across
repositories Intermind is feature comparison
across individuals
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Complex Systems for Health 2

• Emergent Properties of
• Population Health
• No Diagnosis and No Treatment but
• Similar People and Similar Progress
• Viable Health System is
• Adaptive Complex System

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Healthcare Infrastructure

• Infrastructure is the Whole System
• Hospital, Clinic, Home
• Doctors, Nurses, Brochures, Internet
• NO Viable Model for Health System
• Too much Cost! Too Much Volume!

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The Viable Solution

• Need Complete Provider Pyramids
• High Level for High Quality at High Cost
• Low Level for Low Quality at Low Cost
• Handle Volume by Pushing Cases Down
• Bottom Levels handle MOST CASES
• Viable Healthcare Infrastructure
• Hospitals with Doctors for Surgery, Clinics with
  Nurse for Drugs, Homes with Patients for nearly
  all Health Interactions!

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Health Informatics

• Need New Viable Infrastructure
• Health Information Technology
• Provides Support for Patients in Homes
• Creates Bottom of Pyramid to Offload
• Informatics can Solve this Problem
• Patients themselves create population health
  database via informatics that automatically
  routes healthcare

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Informatics Technologies

• Measure Population Health
• Adaptive Question Asking of Quality of Life
  Questionnaires
• Answers for Individuals creates Database for the
  Population
• Manage Population Health
• Structured Health Vectors from normalized patient
  records
• Statistical Information Retrieval cluster
  patients into care cohorts

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Measure Population Health 1

• Quality of Life Questionnaires
• Self-Assessment directly by Patients
• General Status questions, e.g. SF-36
• Specific Disease questions, e.g.
• Arthritis Can you walk without pain?
• Heart Disease Do your ankles swell?
• QoL correctly does coarse prediction
• VA Heart Study SF-12 better than surgeon about
  patient survival

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Measure Population Health 2

• Electronic Records for fine prediction
• Paper supports 10s of questions
• Electronic supports 100s or 1000s
• Adaptive Question Asking
• Choose questions by weighted treewalk
• Each session asks 10s of questions customized to
  particular condition
• Generate Population Database
• Daily individual records from all homes

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Manage Population Health

• Structured Health Vectors
• Patient answers Questions daily
• Average scores generate Health Vector
• Elements of Vector are Meaningful
• Cluster Patient Cohorts
• Normalize Vectors for Similar Clusters
• Weight Question Groups Medically
• Route Care into Pyramid using Clusters to
  Determine Cohorts

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Sample General Health Questions
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Sample Specific Health Questions

• Heart Condition

• Arthritis Condition

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Health Monitor Session
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Beyond Screening

• Why are Some People Healthy? (R. Evans)
• Major categories are disease, health care,
  health function, genetic endowment, physical
  environment, social environment, individual
  response, behavior, well-being, prosperity.
• Healthy People 2010
• 467 objectives in 28 focus areas
• www.health.gov/healthypeople
• Measure Full-Spectrum Health Status
• Detailed QoL in each detailed category

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Population Management

• Possible to Monitor Whole Populations
• Daily Monitors, Full Spectrum of Features
• Internet Software handles Questionnaires
• Cohort Clusters supplement Diagnoses
• Daily Feature Record for each Individual
• Detailed Databases for whole Population
• Analyze Clusters of Similar Patients
• Cohort Switching drive Treatments
• Manage Expectations with Actual Cases
• Improve Health by Switching Cohorts

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Clinical Experiment

• Real Patients in Real Settings
• 1000 senior patients with heart disease
• Use in Medicare Coordinated Care
• Telephone Interface via voice response
• Determine Care Levels automatically
• Demonstrate Feasible Technologies
• Adaptive Question Asking with Faceted Category
  Classification
• Statistical Cohort Clustering with Structured
  Vector Weighting

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Getting from Here to There

• Develop Full-spectrum Questionnaire
• Merge existing Quality of Life instruments
• Encode knowledge from Medical Professionals
• Develop Dynamic Adaptive Administration
• Software to handle Interactive Sessions
• Software to build Individual History
• Software to build Population Database
• Deploy to test Population (100 persons)
• Develop Cohort Similarity Clustering
• Algorithms for Statistical Feature Matching
• Lifestyle Coaching via Cohort Switching

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Healthcare Infrastructure

• Scalable Pilot Project
• 10,000 patients mixed populations for 5 years
• Full-spectrum depth-first for Heart Failure
• Provider Pyramid across County from Center
• Towards Ordinary Medicine
• Handle 1M persons for clinical trial
• Push out from UPMC Health Plan HMO
• Western Pennsylvania, cohorts not diseases
• Automated questionnaires and data analysis

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Healthcare Infrastructure

• Provider Pyramids
• Scale to Volumes for Chronic Illness
• Risk Assessment
• Automatically Determine Level of Care

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Further Reading

• Richard Berlin and Bruce Schatz (2001)
• Population Monitoring of Quality of Life for
  Congestive Heart Failure, Congestive Heart
  Failure, 7(1)13-21 (Jan/Feb 2001).
• Colleen McHorney (1997)
• Generic Health Measurement Past Accomplishments
  and a Measurement Paradigm for the 21st Century,
  Annuals Internal Medicine,127743-750.
• R. Evans, M. Barer, T. Marmor (eds)
• Why are some People Healthy and Others Not?
• The Determinants of Health of Populations
• (New York Aldine de Gruyter, 1990).
• G. Rose, The Strategy of Preventive Medicine
• (Oxford University Press, 1992).