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

Vipul Kashyapvkashyap1@partners.org

Senior Medical Informatician1Clinical Informatics R&D, Partners Healthcare System

Semantic Web and Databases Workshop, ICDE 2006April 8th , 2006

* Thanks to Dr. Tonya Hongsermeier and Alfredo Morales

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

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

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

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 Oct;103(4):596-9

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

KnowledgeApplication

Knowledge Discovery

KnowledgeAsset Management

HealthCare and Life Sciences: A Knowledge Driven EndeavorE.g., Application of Clinical/Genomic Decision Support Rules

E.g., Creation and Maintenance of Clinical Decision Support Rules

E.g., Analysis of clinical Care transactions for Rules, Patient Groups,Potential Biomarkers

Use Case:Dr. 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 StudyReports

Actionable Decision Support

Echo triggers guidance to screen for possible mutations:- MYH7, MYBPC3, TNN2, TNNI3, TPM1, ACTC, MYL2, MYL3

Knowledge-based Decision Support

Connecting Dx, Rx, Outcomes and

Prognosis Data to Genotypic Data for Cardiomyopathy

statisticsapplication

server

statisticsapplication

server

Gene expression in HCM Test Results

Myectomy

Atrial Arrhythymi

ER visits

Clinic visits

Outcomes calculated every weekSyncopeER visit

microarray (encrypted)

ownershipmanager

encryption

Palpitations

Gene-Chips

populationregistry

databasedatabase

microarray (encrypted)

Ventricular Arrhy

ICD

Cong. Heart Failure

ER Visit

EKGCardiac Arr

Thalamus

person concept date

Gene-ChipsEchocardio

CardiomyopAtrial Fib.Echocardio

Z5937XZ5937XZ5937XZ5937X

Z5956XZ5956XZ5956XZ5956X

Z5956XZ5956XZ5956XZ5956X

Z5937X

raw value

3/43/43/43/4

3/93/93/93/9

5/25/25/25/2

4/6

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

Clinical Knowledge

Genomic KnowledgeFigure reprinted withpermission from Cerebra, Inc.

A Strawman OntologyOWL ontologies that blend knowledgefrom the Clinical and Genomic Domains

Data Integration

Domain Ontologiesfor Translational Medicine Research

RPDR GIGPAD Study

RDF Wrapper RDF Wrapper

RDF Graph 1 RDF Graph 2

Merged RDF Graph

Instantiation

Use of RDF graphs that instantiate these ontologies:-- Rules/semantics-based integration independent of location, method of access or underlying data structures!- Highly configurable, minimize

software coding

Bridging Clinical and Genomic Information

“Paternal” 1

type degree

Patient(id = URI1)

“Mr. X”

name

Person(id = URI2)

related_to

FamilyHistory(id = URI3)

has_family_history

“Sudden Death”problem

associated_relative

EMR Data

Patient(id = URI1)

MolecularDiagnosticTestResult(id = URI4)

has_structured_test_result

MYH7 missense Ser532Pro(id = URI5)

identifies_mutation

DialatedCardiomyopathy(id = URI6)

indicates_disease

LIMS Data

Rule/Semantics-based Integration:- Match Nodes with same Ids- Create new links: IF a patient’s structured test result indicates a disease THEN add a “suffers from link” to that disease

90%

evidence1

95%

evidence2

Bridging Clinical and Genomic Information

Patient(id = URI1)

“Mr. X”

name

Person(id = URI2)

related_to

FamilyHistory(id = URI3)

has_family_history

“Sudden Death”problem

“Paternal” 1

type degree

associated_relative

StructuredTestResult(id = URI4)

MYH7 missense Ser532Pro(id = URI5)

identifies_mutation

DialatedCardiomyopathy(id = URI6)

indicates_disease

has_structured_test_result

suffers_from

has_gene

RDF Graphs provide a semantics-rich substrate for decision support. Can be exploited by SWRL Rules

90%, 95%

evidence

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…

Clinical and Genomic Decision SupportIF the patient’s LDL test result is greater than 120AND the patient has a contraindication to Fibric AcidTHEN 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: Clinical+Genomic 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: XYZ3:Ser@$#Pro

Please note: Hypothetical – assume a genetic variant is a biomarker for patients contraindicated to Fibric Acid.

Clinical Decision Support: A Rules-based Implementation

Business Object Model Design

Class Patient: Personmethod 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 StructuredTestResultmethod get_patient(): Patient;method indicates_disease(): Disease;method identifies_mutation(): set of string;method evidence_of_mutation(string): real;

Class LiverPanelResultmethod get_patient(): Patient;method get_ALP(): real;method get_ALT(): real;method get_AST(): real;method get_Total_Bilirubin(): real;method get_Creatinine(): real;

Clinical Decision Support: A Rules-based Implementation

Rule base Design

IF the_patient.has_ldl_result() > 120

AND ((the_patient.has_liver_panel_result().get_ALP() <NormalRange> AND the_patient.has_liver_panel_result().get_ALT() <NormalRange> AND the_patient.has_liver_panel_result().get_AST() <NormalRange> AND the_patient.has_liver_panel_result().get_Total_Bilirubin() <NormalRange> AND the_patient.has_liver_panel_result().get_Creatinine() <NormalRange>) OR “Fibric Acid Allergy” the_patient.has_allergy() OR “Missense: XYZ3:Ser@$#Pro” the_patient.has_mutation())THEN

the_patient.set_therapy(“Zetia Lipid Management Protocol”)

Definition of “Fibric Acid Contraindication”

Clinical Decision Support:Definitions 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.

Clinical Decision Support:Decoupling 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..

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

When Knowledge Changes…

How quickly can you change the content of your rules, order sets, templates, and reports?

The “Genetic Revolution” Begins

June 25, 2003Roche Holding AG is launching the first gene test able to predict how a personwill react to a large range of commonly prescribed medicines, one of the biggestforays yet into tailoring drugs to a patient's genetic makeup.The test is part of an emerging approach to treatment that health expertsexpect could lead to big changes in the way drugs are developed, marketed andprescribed. For all of the advances in medicine, doctors today determine thebest medicine and dose for an ailing patient largely by trial and error. Thefast-growing field of "personalized" medicine hopes to remove such risks andalter the pharmaceutical industry's more one-size-fits-all approach in makingand selling drugs.

Leading the News: Roche Test Promises to Tailor Drugs to Patients --- Precise Genetic Approach Could Mean Major Changes In Development, Treatment

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

Knowledge Maintenance and Provenance

IF the_patient.has_ldl_result() > 120

AND “Fibric Acid Contraindication” the_patient.has_contraindication()

THEN

set the_patient.has_therapy(“Zetia Lipid Management Protocol”)

implemented in an ontology engine

Domain Ontology

Patient_with_Biomarker

has_mutation: “Missense: XYZ3:Ser@$#Pro”

Bridge – Composition Ontology

Rule base

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?

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!

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

Shameless Marketing Plug

Tutorial Presentation at WWW 2006, Edinburgh, UK

Semantics for the HealthCare and Life Sciences

- Vipul Kashyap, Eric Neumann and Tonya Hongsermeier