From the Bench to the Bedside: The role of Semantics in enabling the vision of Translational Medicine

1
From the Bench to the BedsideThe role of
Semantics in enabling the vision of Translational
Medicine

• Vipul Kashyap
• vkashyap1_at_partners.org
• Senior Medical Informatician
• 1Clinical Informatics RD, Partners Healthcare
  System
• Semantic Web and Databases Workshop, ICDE 2006
• April 8th , 2006

Thanks to Dr. Tonya Hongsermeier and Alfredo
Morales
2
Outline

• What is Translational Medicine?
• Current State
• HCLS A Knowledge Driven Endeavor
• Use Case
• Functional Requirements
• Data Integration
• Clinical Decision Support
• Knowledge Change and Provenance
• Conclusions

3
What is Translational Medicine?

• Improve communication between basic and clinical
  science so that more therapeutic insights may be
  derived from new scientific ideas - and vice
  versa.
• Testing of theories emerging from preclinical
  experimentation are tested on disease-affected
  human subjects.
• Information obtained from preliminary human
  experimentation can be used to refine our
  understanding of the biological principles
  underpinning the heterogeneity of human disease
  and polymorphism(s).
• http//www.translational-medicine.com/info/about

4
Current State

• 17 year innovation adoption curve from Discovery
  ? Practice
• Even if a standard is accepted
• 50 chance of receiving inappropriate care
• 5-10 chance of preventable, anticipatable
  adverse event pati
• Healthcare inflation, increasing resistance for
  reimbursement of, new diagnostics and
  therapeutics

5
Current state Knowledge Processing Requirements

• Medical literature doubling every 19 years
• Doubles every 22 months for AIDS care
• 2 Million facts needed to practice
• Genomics, Personalized Medicine will increase the
  problem exponentially
• Typical drug order today with decision support
  accounts for, at best, Age, Weight, Height, Labs,
  Other Active Meds, Allergies, Diagnoses
• Today, there are 3000 molecular diagnostic tests
  on the market, typical HIT systems cannot support
  complex, multi-hierarchical chaining clinical
  decision support

Covell DG, Uman GC, Manning PR. Ann Intern Med.
1985 Oct103(4)596-9
6
Current State Healthcare IT Vendors

• Knowledge hardwired into applications
• Little or no standardization on terminologies or
  information models
• Knowledge engineering tools update content in
  transactional environments, no support for
  versioning, provenance, change propagation.
• Clinical systems implementations have inadequate
  knowledge to meet current workflow and quality
  needs
• Labor of converting knowledge into Clinical
  Decision Support is vastly underestimated

7
HealthCare and Life Sciences A Knowledge
Driven Endeavor
E.g., Application of Clinical/Genomic
Decision Support Rules
Knowledge Application
E.g., Analysis of clinical Care transactions for
Rules, Patient Groups, Potential Biomarkers
Knowledge Discovery
Knowledge Asset Management
E.g., Creation and Maintenance of
Clinical Decision Support Rules
8
Use CaseDr. Genomus Meets Basketball Player
who fainted at Practice

• Clinical exam reveals abnormal heart sounds
• Family History Father with sudden death at 40,
• 2 younger brothers apparently normal
• Ultrasound ordered based on clinical exam reveals
  cardiomyopathy

Structured Physical Exam
Structured Family History
Structured Imaging Study Reports
9
Actionable Decision Support
Echo triggers guidance to screen for possible
mutations - MYH7, MYBPC3, TNN2, TNNI3, TPM1,
ACTC, MYL2, MYL3
10
Knowledge-based Decision Support

• Connecting Dx, Rx, Outcomes and
• Prognosis Data to Genotypic Data for
  Cardiomyopathy

Gene expression in HCM Test Results
person
concept
date
raw value
Z5937X
3/4
Outcomes calculated every week
Syncope
microarray (encrypted)
Myectomy
ER visit
Z5937X
3/4
Atrial Arrhythymi
Palpitations
Z5937X
3/4
ER visits
Gene-Chips
Z5937X
3/4
Clinic visits
Ventricular Arrhy
Echocardio
Z5937X
4/6
ICD
Gene-Chips
Z5956X
5/2
Cong. Heart Failure
microarray (encrypted)
Cardiomyop
Z5956X
5/2
Atrial Fib.
Z5956X
5/2
Echocardio
Z5956X
5/2
EKG
Z5956X
3/9
Cardiac Arr
Z5956X
3/9
ER Visit
Z5956X
3/9
Thalamus
Z5956X
3/9
11
Functional Requirements

• Data Integration
• Advantages of RDF
• Incremental, cost effective approach
• Clinical Decision Support
• Classification v/s Actionable Decision Support
• Advantages of ontology-based inferences
• Knowledge Maintenance and Change Propagation
• Advantages of using ontology-based inferences

12
A Strawman Ontology
OWL ontologies that blend knowledge from the
Clinical and Genomic Domains
Clinical Knowledge
Figure reprinted with permission from Cerebra,
Inc.
Genomic Knowledge
13
Data Integration
14
Bridging Clinical and Genomic Information
Paternal
1
90
type
degree
evidence1

• Rule/Semantics-based Integration
• Match Nodes with same Ids
• Create new links IF a patients structured test
  result indicates a disease
• THEN add a
  suffers from link to that disease

15
Bridging Clinical and Genomic Information
90, 95
evidence
Paternal
Dialated Cardiomyopathy (id URI6)
suffers_from
1
Mr. X
type
degree
name
indicates_disease
related_to
has_structured_test_result
Patient (id URI1)
Person (id URI2)
StructuredTestResult (id URI4)
identifies_mutation
associated_relative
has_family_history
has_gene
problem
MYH7 missense Ser532Pro (id URI5)
FamilyHistory (id URI3)
Sudden Death
RDF Graphs provide a semantics-rich substrate for
decision support. Can be exploited by SWRL Rules
16
Advantages

• RDF Graph based data model
• More expressive than the tree based XML Schema
  Model
• RDF Reification
• Same piece of information can be given different
  values of belief by different clinical genomic
  researchers
• Potential for Schema-less Data Integration
• Hypothesis driven approach to defining mapping
  rules
• Can define mapping rules on the fly
• Incremental approach for Data Integration
• Ability to introduce new data sources into the
  mix incrementally at low cost
• Use of Ontology to disallow meaningless mapping
  rules?
• For e.g., mapping a gene to a protein

17
Clinical and Genomic Decision Support

• IF the patients LDL test result is greater than
  120
• AND the patient has a contraindication to Fibric
  Acid
• THEN
• Prescribe Zetia Lipid Management Protocol
• Contraindication to Fibric Acid Clinical
  Definition (Old)
• The patient is contraindicated for Fibric Acid if
  he has an allergy to Fibric Acid or has elevated
  Liver Panel
• Contraindication to Fibric Acid ClinicalGenomic
  Definition (New)
• The patient is contraindicated for Fibric Acid if
  he has an allergy to Fibric Acid or has elevated
  Liver Panel or has a genetic mutation Missense
  XYZ3Ser_at_Pro
• Please note Hypothetical assume a genetic
  variant is a biomarker for patients
  contraindicated to Fibric Acid.

18
Clinical Decision Support A Rules-based
Implementation

• Business Object Model Design
• Class Patient Person
• method get_name() string
• method has_genetic_test_result()
  StructuredTestResult
• method has_liver_panel_result()
  LiverPanelResult
• method has_ldl_result() real
• method has_contraindication() set of string
• method has_mutation() string
• method has_therapy() set of string
• method set_therapy(string) void
• method has_allergy() set of string
• Class StructuredTestResult
• method get_patient() Patient
• method indicates_disease() Disease
• method identifies_mutation() set of string
• method evidence_of_mutation(string) real

19
Clinical Decision Support A Rules-based
Implementation

• Rule base Design
• IF the_patient.has_ldl_result() gt 120
• AND ((the_patient.has_liver_panel_result().get_ALP
  () ? ltNormalRangegt
• AND the_patient.has_liver_panel_result().g
  et_ALT() ? ltNormalRangegt
• AND the_patient.has_liver_panel_result().g
  et_AST() ? ltNormalRangegt
• AND the_patient.has_liver_panel_result().g
  et_Total_Bilirubin() ? ltNormalRangegt
• AND the_patient.has_liver_panel_result().g
  et_Creatinine() ? ltNormalRangegt)
• OR Fibric Acid Allergy ?
  the_patient.has_allergy()
• OR Missense XYZ3Ser_at_Pro ?
  the_patient.has_mutation())
• THEN
• the_patient.set_therapy(Zetia Lipid
  Management Protocol)

20
Clinical Decision SupportDefinitions vs
Decisions

• Commonly occurring design pattern
• The definition of a Fibric Acid
  Contraindication is represented using rules.
• The decision related to therapeutic intervention
  is also represented using rules.
• Currently, both these inferences are performed by
  the rules engine.

21
Clinical Decision SupportDecoupling definitions
vs decisions

• Evaluation of classification based inferences
  (does patient have a fibric acid
  contraindication?) can be evaluated by an
  ontology engine.
• Reduces overhead on Rule Engine
• Opens up the possibility of plugging-in other
  specialized inference engines (e.g.,
  spatio-temporal conditions)
• Makes knowledge maintenance easier
• Each definition may be referred to in 100s of
  rules..

22
Knowledge Provenance and Maintenance

• There is a close interrelationship between
  knowledge change and provenance
• What has changed? Change
• Why did it change? Provenance
• Did someone change it? Provenance
• Did its components change? Change
• Who changed it? Provenance
• Significance
• Rapid Knowledge Discovery and Evolution in
  Healthcare and Life Sciences

23
When Knowledge Changes
How quickly can you change the content of your
rules, order sets, templates, and reports?
24
The Genetic Revolution Begins
Leading the News Roche Test Promises to Tailor
Drugs to Patients --- Precise Genetic Approach
Could Mean Major Changes In Development,
Treatment
June 25, 2003 Roche Holding AG is launching the
first gene test able to predict how a person will
react to a large range of commonly prescribed
medicines, one of the biggest forays yet into
tailoring drugs to a patient's genetic
makeup. The test is part of an emerging approach
to treatment that health experts expect could
lead to big changes in the way drugs are
developed, marketed and prescribed. For all of
the advances in medicine, doctors today determine
the best medicine and dose for an ailing patient
largely by trial and error. The fast-growing
field of "personalized" medicine hopes to remove
such risks and alter the pharmaceutical
industry's more one-size-fits-all approach in
making and selling drugs.
25
Knowledge Changes

• Introduction of a new molecular diagnostic test
  that identifies genetic variants that have been
  established as biomarkers for patients
  contraindicated for fibric acid.
• Clinical (Old) definition of Fibric Acid
  Contraindication ? Clinical/Genomic (New)
  definition of Fibric Acid Contraindication
• Change in definitions of clinical normality,
  e.g., change in the definition of normal value
  ranges of AST Results
• say from 0 ? AST ? 40 ? 20 ? AST ? 40

26
Knowledge Maintenance and Provenance

• IF the_patient.has_ldl_result() gt 120
• AND Fibric Acid Contraindication ?
  the_patient.has_contraindication()
• THEN
• set the_patient.has_therapy(Zetia Lipid
  Management Protocol)

27
Domain Ontology
28
Bridge Composition Ontology
Rule base
29
Knowledge Change and Provenance

• At each stage, Knowledge Engineer gets notified
  of
• What has changed?
• The definition of Fibric Acid Contraindication
• Why did it change?
• Fibric Acid Contraindication ? Patient with
  Abnormal Liver Panel ? Abnormal Liver Panel ?
  Abnormal AST ? Change in AST Values
• Fibric Acid Contraindication ? Patient with
  Biomarker ? Patient with a particular genetic
  variant
• Who/What was responsible for the change?
• Knowledge Engineer who entered the changed AST
  values?
• Change in a Clinical Guideline?

30
Semantics of Knowledge Maintenance

• Managing change and provenance is a very
  difficult problem
• Semantics can play a crucial role in it
• A reasoner can navigate a semantic model of
  knowledge and propagate change
• One can declaratively change the model at any
  time
• The reasoner will compute the new changes!
• Configuration v/s coding. Could read to a huge
  ROI!

31
Conclusions

• Healthcare and Life Sciences is a knowledge
  intensive field. The ability to capture semantics
  of this knowledge is crucial for implementation.
• Incremental and cost-effective approaches to
  support as needed data integration need to be
  supported.
• Scalable and modular approaches for decision
  support need to be designed and implemented.
• The rate of Knowledge Updates will change
  drastically as Genomic knowledge explodes.
  Automated Semantics-based Knowledge Update and
  Propagation will be key in keeping the knowledge
  updated and current
• Personalized/Translational Medicine cannot be
  implemented in a scalable, efficient and
  extensible manner without Semantic Web
  technologies

32
Shameless Marketing Plug

• Tutorial Presentation at WWW 2006, Edinburgh, UK
• Semantics for the HealthCare and Life Sciences
• - Vipul Kashyap, Eric Neumann and Tonya
  Hongsermeier