# Presentation: 537904

## Multidisciplinary Science, Data Integration, and Large-scale Inferencing: The Role of Informatics Standards

**NCRR Council**
**18 January 2007**
- CG Chute, MD DrPH
- Chair, Biomedical Informatics
- Mayo Clinic College of Medicine

## From Practice-based Evidenceto Evidence-based Practice

- Patient
- Encounters

- Clinical & Genomic Registries

- Data Warehouses & Marts

- Clinical
- Guidelines

- Expert
- Systems

- Informatics
- Standards & Application

- Data

- Inference

- Knowledge
- Management

- Decision
- Support

- Biomedical Knowledge

- © Mayo Clinic College of Medicine 2007

## Outline

- Biomedicine is emerging as “big science”
- Basic biomedical and clinical data have considerable complexity
- Data sharing and synergistic inferencing are key
- Data standards are a key infrastructure
- Ontologies really matter
- Barriers to standards are often cultural
- Leadership is needed to break through cultural barriers

- © Mayo Clinic College of Medicine 2007

## Context of Chris

- Standards Community
- Chair, US ISO delegation
- Co-Chair, HL7 Vocabulary
- Convenor, ISO Semantic Content WG
- Chair, WHO ICD-11 Revision Steering Committee
- Board, ANSI-HHS Health Information Standards Planning Panel
- Chair, CSR BCHI Study Section

- Mayo (real world) Grounding
- Director, Cancer Center Informatics
- PI, caBIG
- Director, CTSA Biomedical Informatics
- Proj PI, NCBO – Roadmap
- Chair, Division of Biomedical Informatics
- Co-Chair, Scientific Advis. - Individualized Med
- Member, IT Oversight

- © Mayo Clinic College of Medicine 2007

## Mayo: A Century-Long Tradition of Studying Patient Outcomes 

- Demographics
- Diagnoses
- Procedures
- Narratives
- Laboratories
- Pathology...

- High-Volume
- Data Storage

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## SemanticOrganization

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## Informatics & Standards Foundations

**Security**

- Patient Data

**Data Governance**

**Meta Data, Standards **
**and Ontologies (informatics)**

- © Mayo Clinic College of Medicine 2007

## The Genomic Era

- The genomic transformation of medicine far exceeds the introduction of antibiotics and aseptic surgery
- The binding of genomic biology and clinical medicine will accelerate
- The implications for *shared semantics* across the basic science and clinical communities are unprecedented

- © Mayo Clinic College of Medicine 2007

## Communities of Scientific EndeavorSilos, Continuity, Synergy

**Chasm of Semantic Despair**

- © Mayo Clinic College of Medicine 2007

## Knowledge, Science, and ComputingKnowledge Explosion

- The volume of facts, assertions, beliefs, and data resources exceeds any human precedent
- Knowledge accumulation continues to accelerate – exponential growth
- Knowledge access is now possible
- Globally – time and space are not barriers
- Personally – Just in time to a screen near you
- Indexed – imagine life before MedLine and Google
- Synthesizable – computing resources proliferate

- © Mayo Clinic College of Medicine 2007

## Knowledge, Science, and ComputingComputing

- Moore’s law still reigns
- 109 fold increase in computing power / 40 yrs
- World-class Supercomputers of 1997
- Game platforms of 2007 – Sony PS3 (IBM Cell 2TF)
- Extensions to networks, memory, & storage
- 110 baud to 10Gb backbone (108 / 35 yrs)
- Vacuum tubes to Gb chips (109 / 40 yrs)
- Paper-tape to Tb drives (1012 / 40 yrs)
- Synergistic summary – 1038 / 40 yrs

- © Mayo Clinic College of Medicine 2007

## What are the Implications ofComputing-Enabled Science?

- Legacy of “lone investigator” may be historical
- Connected, networked data
- Connected, networked investigators
- Synergistic progress is accelerated
- Large-scale projects are more common
- caBIG – NCI Bioinformatics Grid
- PharmGKB – NIGMS Pharmacogenomics
- CTSA – Clinical Translational Science Award

- © Mayo Clinic College of Medicine 2007

## Migrations to “Big Science”

- Physics community
- Rutherford “tabletop” to international accelerators
- Astronomy community
- Hand-held telescopes to Hubble and massive arrays
- Physics and Astronomy spawned the World Wide Web
- Tim Berners-Lee was an astronomer at CERN

- © Mayo Clinic College of Medicine 2007

## The Impact of Digital Science:An Enabling Aspect of Big Science?

- Physics and Astronomy focus on datasets
- Biology and Medicine are aggregating larger datasets and electronic knowledge resources
- Mechanisms for analyzing huge (terabyte) level datasets are now commonplace
- Distributed computing and Grids:
- Networked data – data Grids
- Networked computational power – computing Grids

- © Mayo Clinic College of Medicine 2007

## Issues and Challenges:System Design and Common Models

- If biomedicine is becoming “big” and digital...
- Methods, data, and information workflow must scale across the enterprise
- Ad hoc solutions must fit into larger informatics architecture
- Data and results must be “interoperable”
- If Translational Research is to succeed, we must bridge data and knowledge from research to practice

- © Mayo Clinic College of Medicine 2007

## Information Standards:The Bedrock of Interoperability

- Railroads to the Internet – standards-based successes
- Clinical Standards – highest profile activity in HHS today
- AHIC – American Health Information Community
- ONC – Office of the National Coordinator
- HITSP – Health Informatics Technology Standards Panel
- Research-based standards emerging - caBIG

- © Mayo Clinic College of Medicine 2007

## Implications of Science and Computing

- Science is a repeatable pattern of discovery or experimentation
- Such patterns must be grounded in consistent and comparable representations of reality
- To abstract reality consistently, we require semiotics – signs, symbols, or communications
- To express abstract concepts consistently, we require ontologies
- Ontologies are the bedrock of the scientific method in an information-intensive era

- © Mayo Clinic College of Medicine 2007

## Disease UnderstandingConstrained by Knowledge

- Carolus Linnaeus
- 	Carl von Linné
*Genera Morborum *(1763)
- Underscored Content Difficulty
- Pathophysiology vs. Manifestation
- 	e.g., Rabies as psychiatric disease

- © Mayo Clinic College of Medicine 2007

## Continuum from Nomenclature to Classification

- Patient Data is Highly Detailed
- Modifiers: Anatomy, Stage, Severity, Extent
- Qualifiers: Probability, Temporal Status
- Aggregate Uses Require Categorization
- Granularity of Classifiers
- Focused Groups and Strata for CQI/Outcomes
- Broad Statistical/Fiscal Groups

- © Mayo Clinic College of Medicine 2007

## Familiar Points Along a Continuum: Modern Health Vocabularies

- Nomenclature – Highly Detailed Descriptions (SNOMED)
- Classification – Organized Aggregation of Descriptions into a Rubric (ICDs)
- Groupings – High-Level Categories of Rubrics (DRGs)

- Detailed

- Grouped

- Nomenclature

- Classification

- Groups

- © Mayo Clinic College of Medicine 2007

## Molecular

**Clinical**

- Fine Detail

**Highly Aggregated**

***has translation***

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## Blois, 1988Medicine and the nature of vertical reasoning 

- Molecular: receptors, enzymes, vitamins, drugs
- Genes, SNPs, gene regulation
- Physiologic pathways, regulatory changes
- Cellular metabolism, interaction, meiosis,...
- Tissue function, integrity
- Organ function, pathology
- Organism (Human), disease
- Sociology, environment, nutrition, mental health...

- © Mayo Clinic College of Medicine 2007

## Multidisciplinary Miscommunication:Statistics and Machine Learning

- Long tradition of statistical inferencing
- Evolve from stochastic models
- Algorithms of iterative approximation
- Maximum Likelihood Estimator (MLE)
- Logistic Regression (multifactorial, binomial)
- Computer Science innovates inferencing
- Machine Learning techniques
- Algorithms of iterative approximation
- Neural Networks (feature selection)
- Huge controversy over relative superiority

- © Mayo Clinic College of Medicine 2007

## Unrecognized Equivalence:Feudal Semantics 

- Nested logistic regression (stats)
- Neural Networks (machine learning)
- Mathematically equivalent
- Discovered after 10 years
- Obscured by discordant technical vocabulary
- An instance of a semantic chasm

- © Mayo Clinic College of Medicine 2007

## Feudal Cognition:Intellectual Semantic Baronies 

- Genetic variation – Genomics
- Haplotypes – Statistical Genomics
- Molecular – Metabolomics, Proteomics
- Binding – Molecular simulation
- Pathways – Physiology and Systems Biology
- Symptoms – Consumer Health
- Rx and Px – Clinical Medicine
- Risk – Public Health, Epidemiology
- Social impact – Sociology, Health Economics

- © Mayo Clinic College of Medicine 2007

## Whither Phenotype? Spans spectrum from enzymes to disease

- Pharmacogenomics – enzyme functionality
- Physiologist – cellular function
- Systems biologist – pathway circuit flow
- Sub-specialist – organ functioning
- Patient/Clinician – disease manifestation
- Public Health – population characteristics
*Highly specific to use-case context*

- © Mayo Clinic College of Medicine 2007

## Whither Disease as Phenotype?

- Notions of disease are inextricably bound to an underpinning Knowledge Base (rabies)
- Disease manifestation occurs across multiple planes of human understanding
- Boundaries among clinical observational elements* are relativistic, rapidly changing, and probably serve little practical purpose
- Observations, findings, syndromes, diseases, genotype, haplotype, epigenomic change...

- © Mayo Clinic College of Medicine 2007

## Variations in Concept Identity:Orthologs, Paralogs, and SNPs  

- Enzymes that share function
- Sulfotransferase
- Pseudogenes – Concept or degradation
- Orthologs across primates
- Paralogs (including pseudogenes) in humans
- Polymorphisms between individuals
- Data or Ontology Concepts?
- 	Synonymy or Plesionymy

- © Mayo Clinic College of Medicine 2007

## Terminology Standards are Not SufficientStructure and Context must be Accommodated

- Word, Concepts, Findings, and Interventions have CONTEXT
- Context Can be Represented in a Multitude of Legitimate Structures
- Equivalent Contexts Must be Recognizable and Interconvertible

- © Mayo Clinic College of Medicine 2007

## Content vs. StructureYet Another Chasm

*Family History of* Breast Cancer
*Family History of* Heart Disease
*Family History of* Stroke

- Breast Cancer
- Heart Disease
- Stroke

*Family History*

-   Terminologic Model

-   Information Model

- Equivalent Content

- [adapted from Rossi-Mori]** **

- © Mayo Clinic College of Medicine 2007

## Information Model (HL7)Terminology Model (SNOMED)

- 		HL7 RIM	SNOMED CT Attribute
- 	targetSiteCode(Observation)		“finding site”
- 		targetSiteCode(Procedure)	“procedure site”
- 	methodCode
- 		(Observation & Procedure)	“method”
- approachSiteCode(Procedure)	“approach,” “access”
- 		priorityCode(Act)	“priority”

- [adapted from Markwell]** **

- © Mayo Clinic College of Medicine 2007

## Vocabulary and Information ModelsImplicit and Explicit Semantics

- Harmonization represents greatest information challenge in health care
- Present across genomic and patient data
- Obvious in research and clinical use-cases
- Critical for any kind of aggregation
- The TermInfo project as exemplar
- SNOMED and HL7 working on two-way resolution

- © Mayo Clinic College of Medicine 2007

## Aggregation Logics by Domain:Rule-based Aggregations 

**Aggregation Logics by Domain:****Rule-based Aggregations **

- Decision Support and Error Detection

- Public Health and
- Surveillance

- Reimbursement and Management

- Outcome Research and Epidemiology

- Findings

- Interventions

- Events

- © Mayo Clinic College of Medicine 2007

## Semantic Metadata:Annotations for Applications

- Data organization
- Data annotation
- Ontology-driven architectures
- Semantic interoperability
- Data retrieval analytic subsets
- The right data for the problem
- Knowledge-enabled analyses
- The right analyses for the data

- © Mayo Clinic College of Medicine 2007

## Data OrganizationPertains to Clinical and Research sources

- Health IT Standards
- Syntactic – XML, HL7, database schema,
- Semantic – OWL, HL7, ontologies
- Interoperable
- Self-Describing Objects
- Data storage technology (NCRR grant)
- Annotation Metadata
- Provenance
- Data management
- Content, Context, Structure, Semantics

- © Mayo Clinic College of Medicine 2007

## The Communication Process

_**CONCEPT**_

- Referent

*Refers To*

*Symbolises*

*Stands For*

**“****Rose”,**
**“****ClipArt”**

*Refers To*

*Symbolises*

*Stands For*

**“****Rose”,**
**“****ClipArt”**

_**CONCEPT**_

_**Symbol**_

_**Symbol**_

**“****I see a ClipArt image of a rose”**

- Semantics

- Syntax

- Context

- Shared Context

- [From Solbrig]** **

- © Mayo Clinic College of Medicine 2007

## Making Shared Context Explicit

_**CONCEPT**_

- Referent

*Refers To*

*Symbolises*

*Stands For*

**“****Rose”,**
**“****ClipArt”**

*Refers To*

*Symbolises*

*Stands For*

**“****Rose”,**
**“****ClipArt”**

_**CONCEPT**_

_**Symbol**_

_**Symbol**_

**“****I see a ClipArt image of a rose”**

- Formal Shared
- Context

**Terminologies**

**Terminologies**

- [From Solbrig]** **

- © Mayo Clinic College of Medicine 2007

## Proliferation of Content:“Have it your way” Vocabulary Models

- Major ontologies
- SNOMED CT; Gene Ontology; LOINC; NDF-RT
- UMLS Metathesaurus; NCI Thesaurus
- HL7 RIM and Vocabulary; DICOM RadLex
- CDC bioterrorism PHIN standards
- caBIG DSR / CDEs (Common Data Elements)
- All created with differing formats and models
- Mechanisms for content sharing
- Research area

- © Mayo Clinic College of Medicine 2007

## Mayo LexGrid ProjectOntology Services

- HL7 ANSI Standard
- ISO Standard
- Open specification & source
*Provide consistency and standardization required to support large-scale vocabulary adoption and use*
- Common ***model***, tools, formats, and interfaces
- Standard terminology model (Excel to OWL)
- Grid-nodal architecture
- _http://informatics.mayo.edu__ _

- © Mayo Clinic College of Medicine 2007

## Examples and Proof of Concept

- NIH RoadMap: Nat. Center Biomedical Ontologies
- Mayo LexGrid project [MLG]
- Clinical and basic science (Gene Ontology) communities
- NCI caBIG – Bioinformatics Grid [MLG]
- HHS/ONC NHIN National Health Information Network
- IBM Data Coordination project
- NLM/HL7 Coordination project [MLG]
- CDC PHIN Public Health Information Network [MLG]
- ISO 11179 metadata standards [MLG]

- © Mayo Clinic College of Medicine 2007

## NCBO – A Bridge Across the Chasm

- © Mayo Clinic College of Medicine 2007

## NCBO Tools

- © Mayo Clinic College of Medicine 2007

## Ontologies by Category

- © Mayo Clinic College of Medicine 2007

## GO Biological Process Metadata

- © Mayo Clinic College of Medicine 2007

## Search Results

- © Mayo Clinic College of Medicine 2007

## MeSH Hindlimb

- © Mayo Clinic College of Medicine 2007

## Where is This Going?

- Basic science and clinical research in biomedicine are reaching an information critical mass
- Technology can support the semantics and data complexity
- The barriers to interoperable data sharing are cultural
- NCRR and CTSA can herald transformation

- © Mayo Clinic College of Medicine 2007

## Conclusion

- Biomedicine concepts have become complex and intertwined
- Big science model of future research
- Research synergies will depend upon established interoperability standards
- Ontologies as formal models of concepts provide great opportunity
- Tools, content, and resources are becoming increasingly available
**NCRR can provide biomedical standards leadership**

- © Mayo Clinic College of Medicine 2007