Role of Semantic Web in Health Informatics

Role of Semantic Web
in Health Informatics

Tutorial at 2012 ACM SIGHIT International Health Informatics
Symposium (IHI 2012), January 28-30, 2012

Satya S. Sahoo, GQ Zhang AmitSheth
Division of Medical Informatics Kno.e.sis Center
Case Western Reserve University Wright State University

Outline
• Semantic Web
o Introductory Overview
• Clinical Research
o Physio-MIMI
• Bench Research and Provenance
o Semantic Problem Solving Environment for T.cruzi
• Clinical Practice
o Active Semantic Electronic Medical Record

Landscape of Health Informatics

Patient Care
Personalized Medicine
Drug Development
Clinical Research Bench Research
Privacy
Cost

Clinical Practice
* Images from case.edu

Challenges
• Information Integration: Reconcile heterogeneity
o Syntactic Heterogeneity: DOB vs. Date of Birth
o Structural Heterogeneity: Street + Apt + City vs.
Address
o Semantic Heterogeneity: Age vs. Age at time of surgery
vs. Age at time of admission
• Humans can (often) accurately interpret, but
extremely difficult for machine
o Role for Metadata/Contextual Information/Semantics

Semantic Web
• Web of Linked Data
• Introduced by Berners
Lee et. al as next step for
Web of Documents
• Allow “machine
understanding” of data,
• Create “common”
models of domains using
formal language -
Semantic Web Layer Cake
ontologies
Layer cake image source: http://www.w3.org

Resource Description Framework
Location
Company Armonk, New York,
United States
IBM

Zurich, Switzerland

• Resource Description Framework – Recommended by
W3C for metadata modeling [RDF]
• A standard common modeling framework – usable by
humans and machine understandable

RDF: Triple Structure, IRI, Namespace
Headquarters located in Armonk, New York,
IBM
United States

• RDF Triple
o Subject: The resource that the triple is about
o Predicate: The property of the subject that is described by the triple
o Object:The value of the property
• Web Addressable Resource:Uniform Resource Locator (URL), Uniform
Resource Identifier(URI), Internationalized Resource Identifier (IRI)
• Qualified Namespace:http://www.w3.org/2001/XMLSchema#
asxsd:
o xsd: string instead of http://www.w3.org/2001/XMLSchema#string

RDF Representation
• Two types of property values in a triple
o Web resource Headquarters located in
IBM Armonk, New York,
o Typed literal United States

Has total employees
IBM “430,000” ^^xsd:integer

• The graph model of RDF:node-arc-node is the primary
representation model
• Secondary notations: Triple notation
o companyExample:IBM companyExample:has-Total-
Employee “430,000”^^xsd:integer .

RDF Schema
Headquarters located in Armonk, New
IBM
York, United States

Headquarters located in Redwood Shores,
Oracle
California, United States

Headquarters located in
Company Geographical Location

• RDF Schema: Vocabulary for describing groups of
resources [RDFS]

RDF Schema
• Propertydomain(rdfs:domain) and range(rdfs:range)
Domain Headquarters located in Range
Company Geographical Location

• Class Hierarchy/Taxonomy:rdfs:subClassOf
SubClass rdfs:subClassOf (Parent) Class
Computer Technology Company
Company

Banking Company

Insurance Company

Ontology: A Working Definition
• Ontologies are shared conceptualizations of a
domain represented in a formal language*
• Ontologies in health informatics:
o Common representation model - facilitate
interoperability, integration across different projects,
and enforce consistent use of terminology
o Closely reflect domain-specific details (domain
semantics) essential to answer end user
o Support reasoning to discover implicit knowledge

* Paraphrased from Gruber, 1993

OWL2 Web Ontology Language
• A language for modeling ontologies [OWL]
• OWL2 is declarative
• An OWL2 ontology (schema) consists of:
o Entities:Company, Person
o Axioms:Company employs Person
o Expressions:A Person Employed by a Company =
CompanyEmployee
• Reasoning: Draw a conclusion given certain
constraints are satisfied
o RDF(S) Entailment
o OWL2 Entailment

OWL2 Constructs

• Class Disjointness: Instance of class A cannot be
instance of class B
• Complex Classes: Combining multiple classes with
set theory operators:
o Union:Parent =ObjectUnionOf(:Mother :Father)
o Logical negation:UnemployedPerson =
ObjectIntersectionOf(:EmployedPerson)
o Intersection:Mother =ObjectIntersectionOf(:Parent
:Woman)

OWL2 Constructs

• Property restrictions: defined over property
• Existential Quantification:
o Parent =ObjectSomeValuesFrom(:hasChild :Person)
o To capture incomplete knowledge
• Universal Quantification:
o US President = objectAllValuesFrom(:hasBirthPlace
United States)
• Cardinality Restriction

SPARQL: Querying Semantic Web Data

• A SPARQL query pattern composed of triples
• Triples correspond to RDF triple structure, but
have variable at:
o Subject: ?companyex:hasHeadquaterLocationex:NewYork.
o Predicate: ex:IBM?whatislocatedinex:NewYork.
o Object: ex:IBMex:hasHeadquaterLocation?location.
• Result of SPARQL query is list of values –
valuescan replace variable in query pattern

SPARQL: Query Patterns
• An example query pattern
PREFIX ex:<http://www.eecs600.case.edu/>
SELECT?company ?location WHERE
{?company ex:hasHeadquaterLocation?location.}
• Query Result

company location Multiple
Matches
IBM NewYork
Oracle RedwoodCity
MicorosoftCorporation Bellevue

SPARQL: Query Forms
• SELECT: Returns the values bound to the variables
• CONSTRUCT: Returns an RDF graph
• DESCRIBE: Returns a description (RDF graph) of
a resource (e.g. IBM)
o The contents of RDF graph is determined by SPARQL
query processor
• ASK: Returns a Boolean
o True
o False

Semantic Web+Clinical Research Informatics =
Physio-MIMI

Physio-MIMI Overview
• Physio-MIMI: Multi-Modality, Multi-Resource Environment for
Physiological and Clinical Research
• NCRR-funded, multi-CTSA-site project (RFP 08-001) for
providing informatics tools to clinical investigators and clinical
research teams at and across CTSA institutions to enhance the
collection, management and sharing of data
• Collaboration among Case Western, U Michigan, Marshfield
Clinic and U Wisconsin Madison
• Use Sleep Medicine as an exemplar, but also generalizable
• Two year duration: Dec 2008 – Dec 2010

Features of Physio-MIMI
• Federated data integration environment
– Linking existing data resources without a centralized data
repository
• Query interface directly usable by clinical researchers
– Minimize the role of the data-access middleman
• Secure and policy-compliant data access
– Fine-grained access control, dual SSL, auditing
• Tools for curatingPSGs

Data Integration Framework
Physio-MIMI

SHHS Portal

Measure not by the size of the database, but the
number of secondary studies it supported

Query Interface – driven by access
• Visual Aggregator and Explorer (VISAGE)
• Federated, Web-based
• Driven by Domain Ontology (SDO)
• PhysioMap to connect autonomous data sources

Clinical Clinical
Investigator Investigator

1 3 1 • GQ Zhang et al.
VISAGE: A Query Interface for Clinical
Data Analyst
Data Manager Database 3 Research, Proceedings of the 2010 AMIA
Clinical Research Informatics
2
2 Summit, San Francisco, March 12-13, pp.
Data Analyst
76-80, 2010
Database Data Manager

Physio-MIMI Components
Sleep Researcher Domain Expert Informatician
META SERVER

Query Builder
VISAGE

Query Manager Query Explorer

DB-Ontology Mapper
DATA SERVER

Institutional Databases Institutional Databases Institutional Databases

Institutional Firewall Institutional Firewall Institutional Firewall

Case Control Study Design
•Case-control is a common study design
• Used for epidemiological studies involving two cohorts,
one representing the cases
and the second representing the controls
• Adjusting matching ratio to improve statistical power

Example (CFS)
• Suppose we are interested in the question of whether
sleep parameters (EEG) differ by obesity in age and race
matched males
• Case: adult 55-75, male, BMI 35-50 (obese)
• Control: adult 55-75, male, BMI 20-30 (non-obese)
• Matching 1:2 on race (minimize race as a factor initially)

1:2 Matching Result

Control Matched
Case

1:5 Matching – CFS+SHHS

Modify Control to Include
TWO data sources

Sleep Domain Ontology (SDO)
• Standardize terminology and semantics (define variations) [RO]
• Facilitate definition of data elements
• Valuable for data collection, data curation
• Data integration
• Data sharing and access
• Take advantage of progress in related areas (e.g. Gene Ontology)
• Improving data quality – provenance, reproducibility

Sleep Domain Ontology (SDO)
https://mimi.case.edu/concepts

VISAGE Query Builder showing a data query on Parkinsonian Disorders and REM sleep
behavior disorder with race demographics

Semantic Web+Provenance +Bench
Research=T.cruzi SemanticProblem Solving
Environment

Semantic Problem Solving Environment for
T.cruzi

Provenance in Scientific Experiments

New Parasite Strains

Gene
Name

Sequence
Extraction

Drug
3‘ & 5’
Resistant
Region
Plasmid Gene Name
Plasmid
Construction

Knockout
T.Cruzi
Construct Plasmid
sample
?
Transfection

Transfected
Sample

Drug Cloned Sample
Selection

Selected
Sample

Cell
Cloning

Cloned
Sample

Gene
Name
• Provenance from the French word
“provenir” describes the lineage or
Sequence
Extraction

Drug
Resistant
Plasmid
3‘ & 5’
Region history of a data entity
Plasmid
Construction
• For Verification and Validation of
T.Cruzi
Knockout
Construct Plasmid
Data Integrity, Process Quality, and
Trust
sample

Transfection

Transfected
• Semantic Provenance Framework
Sample
addresses three aspects [Prov]
o Provenance Modeling
Drug
Selection

Selected
Sample o Provenance Query Infrastructure
Cell
Cloning o Scalable Provenance System
Cloned
Sample

Domain-specific Provenance ontology
has_agent agent
is_a PROVENIR
data
ONTOLOGY
is_a parameter
data_collection is_a
process is_a
spatial_parameter
temporal_parameter

is_a domain_parameter
is_a is_a

is_a is_a
is_a
transfection_machine
location is_a
drug_selection is_a
subPropertyOf
sample has_temporal_parameter
strain_creation is_a
_protocol Time:DateTime
Descritption
transfection
cell_cloning is_a transfection_buffer
PARASITE
has_input_value Tcruzi_sample EXPERIMENT
ONTOLOGY
has_parameter

• Total Number of Classes - 118
• DL Expressivity – ALCHQ(D)

Provenance Query Classification
Classified Provenance Queries into Three Categories
• Type 1: Querying for Provenance Metadata
o Example: Which gene was used create the cloned sample with ID = 66?
• Type 2: Querying for Specific Data Set
o Example: Find all knockout construct plasmids created by researcher
Michelle using “Hygromycin” drug resistant plasmid between April 25, 2008
and August 15, 2008
• Type 3: Operations on Provenance Metadata
o Example: Were the two cloned samples 65 and 46 prepared under
similar conditions – compare the associated provenance
information

Provenance Query Operators
Four Query Operators – based on Query
Classification
• provenance () – Closure operation, returns the
complete set of provenance metadata for input data
entity
• provenance_context() - Given set of constraints
defined on provenance, retrieves datasets that
satisfy constraints
• provenance_compare () - adapt the RDF graph
equivalence definition
• provenance_merge () - Two sets of provenance
information are combined using the RDF graph
merge

Answering Provenance Queries using provenance ()
Operator

Implementation: Provenance Query Engine
QUERY
OPTIMIZER

• Three modules:
o Query Composer
o Transitive closure
o Query Optimizer
• Deployable over a
RDF store with
support for
reasoning TRANSITIVE CLOSURE

Application in T.cruzi SPSE Project

• Provenance tracking
for gene knockout,
strain creation,
proteomics, microarray
experiments
• Part of the Parasite
Knowledge Repository
[BKR]

W3C Provenance Working Group
• Define a “provenance interchange language for
publishing and accessing provenance”
• Three working drafts:
o PROV-Data Model: A conceptual model for
provenance representation
o PROV-Ontology: An OWL ontology for provenance
representation
o PROV-Access and Query: A framework to query
and retrieve provenance on the Web

Semantic Web+Clinical Practice Informatics =Active
Semantic Electronic Medical Record (ASEMR)

Semantic Web application in use
In daily use at Athens Heart Center
– 28 person staff
• Interventional Cardiologists
• Electrophysiology Cardiologists
– Deployed since January 2006
– 40-60 patients seen daily
– 3000+ active patients
– Serves a population of 250,000 people

Information Overload in Clinical
Practice
• New drugs added to market
– Adds interactions with current drugs
– Changes possible procedures to treat an illness
• Insurance Coverage's Change
– Insurance may pay for drug X but not drug Y even
though drug X and Y are equivalent
– Patient may need a certain diagnosis before some
expensive test are run
• Physicians need a system to keep track of ever
changing landscape

System though out the practice

Active Semantic Document (ASD)
A document (typically in XML) with the following features:

• Semantic annotations
– Linking entities found in a document to ontology
– Linking terms to a specialized lexicon [TR]

• Actionable information
– Rules over semantic annotations
– Violated rules can modify the appearance of the document (Show an
alert)

Active Semantic Patient Record
• An application of ASD
• Three Ontologies
– Practice
Information about practice such as patient/physician data
– Drug
Information about drugs, interaction, formularies, etc.
– ICD/CPT
Describes the relationships between CPT and ICD codes
• Medical Records in XML created from database

Practice Ontology Hierarchy
(showing is-a relationships)

facility
insurance_
ancillary owl:thing carrier

ambularory insurance
_episode
insurance_
encounter
plan
person

event insurance_
patient policy
practitioner

Drug Ontology Hierarchy
(showing is-a relationships)

formulary_
non_drug_ interaction_ property formulary
reactant property
indication
indication_ property
owl:thing
monograph property
_ix_class prescription interaction_
_drug_ with_non_
brandname_ prescription
brand_name drug_reactant
prescription individual _drug interaction
_drug_
property brandname_
brandname_ composite prescription interaction_
undeclared _drug_ with_mono
interaction_
generic graph_ix_cl
with_prescri
cpnum_ generic_ ass
ption_drug
group composite
generic_
individual

Drug Ontology showing neighborhood of
PrescriptionDrug concept

Part of Procedure/Diagnosis/ICD9/CPT Ontology

maps_to_diagnosis
specificity
procedure
diagnosis

maps_to_procedure

Semantic Technologies in Use
• Semantic Web: OWL, RDF/RDQL, Jena
– OWL (constraints useful for data consistency), RDF
– Rules are expressed as RDQL
– REST Based Web Services: from server side
• Web 2.0: client makes AJAX calls to ontology, also auto
complete
Problem:
• Jena main memory- large memory footprint, future scalability
challenge
• Using Jena’s persistent model (MySQL) noticeably slower

Benefits: Athens Heart Center Practice
Growth
1400
1300
1200
1100
Appointments

1000 2003
900
2004
800
2005
700
2006
600
500
400

v
b

g
r

c
n

n

l

p
ar

t
ay

ju
ap

no
oc
fe
ja

ju

au

de
se
m

m

Month

Chart Completion before the preliminary
deployment of the ASMER

600
500
400
Charts

Same Day
300
Back Log
200
100
0
Se 4

5
04

05
04

05
04

05
04

04
l0

l0
n

n
ay

ay
pt
ar

ar
ov
Ju

Ju
Ja

Ja
M

M
M

M
N

Month/Year

Chart Completion after the preliminary
deployment of the ASMER

700
600
500
Charts

400 Same Day
300 Back Log
200
100
0
Sept Nov 05 Jan 06 Mar 06
05
Month/Year

Benefits of current system
• Error prevention (drug interactions, allergy)
– Patient care
– insurance
• Decision Support (formulary, billing)
– Patient satisfaction
– Reimbursement
• Efficiency/time
– Real-time chart completion
– “semantic” and automated linking with billing

Demo

On-line demo of Active Semantic Electronic Medical Record

deployed and in use at Athens Heart Center

71

Challenges, Opportunities, and Future
Direction

Conclusions
Benefits of SW in Health Informatics:
• RDF a “universal” data model; Application-
purpose agnostic (clinical care vs research)
• Integration “ready,” supporting distributed query
out of box
• Semantic interoperability addressed at root level
• Better support of user interfaces for data capture,
data query, data integration
• Scalability demonstrated

Challenges and Future Directions
• Design and implementation of health information systems
with RDF as primary data store from ground up
• User-friendly graphical query interface on top of SPARQL
• Managing Protected Health Information (PHI) e.g. data
encryption “at rest” for RDF store
• From retrospective annotation of data (with ontology) to
prospective annotation of data: ontology-driven data capture
with annotation happening at the point of primary source
(eliminating the need to annotate data retrospectively)
• Let ontology drive “everything”

References
• [RDF] Manola F, Miller, E.(Eds.). RDF Primer. 2004; Available from:
http://www.w3.org/TR/rdf-primer/
• [RDFS] Brickley D, Guha, R.V. RDF Schema. 2004; Available from:
http://www.w3.org/TR/rdf-schema/
• [OWL] Hitzler P, Krötzsch, M., Parsia, B., Patel-Schneider, P.F., Rudolph, S. OWL 2
Web Ontology Language Primer: W3C; 2009
• [Physio-MIMI]: http://physiomimi.case.edu
• [ASEMR] A. P. Sheth, Agrawal, S., Lathem, J., Oldham, N., Wingate, H., Yadav, P.,
Gallagher, K., "Active Semantic Electronic Medical Record," in 5th International
Semantic Web Conference, Athens, GA, USA, 2006.
• [BioRDF] BioRDF subgroup: Health Care and Life Sciences interest group Available:
http://esw.w3.org/topic/HCLSIG_BioRDF_Subgroup
• [TR] A. Ruttenberg, et al., "Advancing translational research with the Semantic Web,"
BMC Bioinformatics vol. in Press, 2007.

References 2
• [Visage] GQ Zhang et al. VISAGE: A Query Interface for Clinical Research,
Proceedings of the 2010 AMIA Clinical Research Informatics Summit, San Francisco,
March 12-13, pp. 76-80, 2010
• [Prov] S.S. Sahoo, V. Nguyen, O. Bodenreider, P. Parikh, T. Minning, A.P. Sheth, “A
unified framework for managing provenance information in translational research.”
BMC Bioinformatics 2011, 12:461
• [RO] Smith B, Ceusters W, Klagges B, Kohler J, Kumar A, Lomax J, Mungall C,
Neuhaus F, Rector AL, Rosse C: Relations in biomedical ontologies. Genome Biol
2005, 6(5):R46.
• [BKR] Bodenreider O, Rindflesch, T.C.: Advanced library services: Developing a
biomedical knowledge repository to support advanced information management
applications. In. Bethesda, Maryland: Lister Hill National Center for Biomedical
Communications, National Library of Medicine; 2006.
• T.cruzi project web site: http://wiki.knoesis.org/index.php/Trykipedia

Acknowledgements
• Collaborators:
o Susan Redline, Remo Mueller, and other members of
Physio-MIMI team
o Rick Tarleton, Todd Manning, Priti Parikh and other
members of the T.cruzi SPSE team
o Dr. S. Agrawal and other members at the Athens Heart
Center, GA
• NIH Support: UL1-RR024989, UL1-RR024989-05S,
NCRR-94681DBS78, NS076965, and 1R01HL087795

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