C urrent status of O ntologies in B iomedical and C linical I nformatics. By Rishi Kanth Saripalle Biomedical Informatics, University of Connecticut, Storrs

Current status of Ontologies in Biomedical and Clinical

Informatics.

ByRishi Kanth Saripalle

Biomedical Informatics, University of Connecticut, Storrs1

Overview Aim of the Presentation. Ontology

• Definition and Description.• Example.

Present Biomedical Ontology Need for Integration Application of Biomedical Ontology

• Clinical Trials• OASIS: Integration Technique• Clinical Decision Support System

Summary2BioMedical Informatics

Goal

To study about ontologies, their advantages and applications in the field of

Biomedical and Clinical informatics.

3BioMedical Informatics

Ontology

Definition (From Philosophy) :

According to Philosophy, Ontology is study of

being or existence and forms the basic subject matter of

metaphysics. It seeks to describe the basic categories and

relationships of being or existence to define entities and

types of entities within its framework.


Ontology

In Computer science , Ontology means

“specification of a conceptualization” .It means

“A data model that represents a set of concepts

within a domain and the relationships between those

concepts”.

Definition (From Computer Science) :


Advantages of Ontology

To Share the common structure of information.

To reuse the similar domain Ontology.

Intelligent system can provide reasoning Systems to make inferences within the Ontology.

Semantic way of representing knowledge of the domain.


Determine the domain and Scope ( Range ) of the knowledge.

Look for already existing ontology in the similar domain

Listing all the terminologies or Concepts of the domain

List all the classes and instances to be created in the ontology.

Create the properties which will relate these concepts in the ontology.

Development of Ontology


Wine Australian Yellow Tail

Color Flavor Maker

Grape

Yellow Delicate AustraliaGerman

Class Individual

Property Values

Properties

8

Example of Ontology

BioMedical Informatics

Disease Ontology

Sub-Classes of

Cardiology Diseases

Instances of Mitral_Valve_Disorders

Hierarchical organization of Cardiology Diseases 9BioMedical Informatics

Disease Ontology

Property Defined

Representation of “Mitral_Valve_Prolapse” knowledge using properties and instances 10BioMedical Informatics

Implemented Ontology in OWL Format

…………..

<Congenital_Heart_Disease rdf:ID="Atrial_septal_defect"> <Complications> <Cardiac_Arrhythmias rdf:ID="Arrhythmia"> <Has_Intervention rdf:datatype="http://www.w3.org/2001/XMLSchema#string" >defibrillation</Has_Intervention> <Have_Symptoms> <Cardiology_Symptoms rdf:ID="Dyspnea"/> </Have_Symptoms> <Has_Diagnosis_Test> <Cardiology_Diagnosis_Test rdf:ID="Coronary_Angiography"> <Has_Synonyms rdf:datatype="http://www.w3.org/2001/XMLSchema#string" >coronary catheterization </Has_Synonyms> ……………….. 11BioMedical Informatics

Bio-Medical Ontologies

OpenCyc

WordNet

Galen

UMLS

SNOMED – CT

FMA

Gene Ontology


Open Cyc

Open Cyc is an Upper level ontology developed by Cycorp Inc.

Open Cyc has 60,000 hand coded assertions that capture “common sense language”, so that AI algorithms can perform human like reasoning and contains 6,000 concepts


Word Net WordNet is an electronic lexical database developed at Princeton University that serves as a resource for applications in natural language processing and information retrieval. cancer, malignant neoplastic disease: any malignant growth or tumor caused by abnormal and uncontrolled cell division; it may spread to other parts of the body through the lymphatic system or the blood stream Cancer, Crab: (astrology) a person who is born while the sun is in CancerCancer: a small zodiacal constellation in the northern hemisphere; between Leo and GeminiCancer, Cancer the Crab, Crab: the fourth sign of the zodiac; the sun is in this sign from about June 21 to July 22Cancer, genus Cancer: type genus of the family Cancridae 14BioMedical Informatics

Galen

GALEN stands for Generalised Architecture for Languages, Encyclopedia and Nomenclature in Medicine. It is a European project developed for reuse of terminology in clinical systems.


UMLS

UMLS acronym for Unifies Medical Language System was developed for National Library of Medicine.

Disease is semantic type with around 392 relations (109 semantic relations and 22 other relations). Pneumonia categorized under one semantic type Disease, but has hundreds of relations.


SNOMED-CT

SNOMED stands for Systemized Nomenclature Of Medicine Clinical Terms. SNOMED-CT is the result of merging two ontologies: SNOMED-RT and Clinical Terms.


Need for Integration

All the ontologies developed have a common aim, describing the domain knowledge

Integration of ontologies is becoming very critical as applications tend to use multiple ontologies and concepts in the various ontology overlap or same concept is described in multiple ways. For example, the concept “Blood” is described differently in above discussed ontologies. One describes it as “Fluid”, another as “substance” and another as “semi- solid substance” etc.


Ontology AOntology B

Semantics vs Structural Integration ?

Difficulties of integration arise with similar, same and complementary ontology integration.


OASIS: Ontology Mapping and Integration framework

OASIS


Terms in the OBO ( Open Biomedical Ontology) are arranged in Directed Acyclic Graph. This allows each children to have multiple parents. The arc between the concepts can be IS-A or PART-OF relations.

IOMG – Interactive schema matching algorithm. The first step in this algorithm is to find Similarity Metrics.

Linguistic Similarity Definition Similarity Neighbor Similarity


Applications of Ontology

Randomized Clinical Trials (RCT)

Patient Records based on Clinical Trials.

Clinical Decision Systems.

…………..


Randomized Clinical Trials (RCT) Randomized Clinical Trails: one of the least biased sources of clinical research evidence, and are therefore a critical resource for the practice of evidence-based medicine

Scientific community is trying to encode the finding in computer process able language.

However, for evidence to be put in practice one has to analysis the data. The canonical practice for trial interpretation is call System Reviewing.

Source for Data Specification:– Trial Reports– Trial Databases.


Life Cycle of Clinical Trials

Ontology Specifications


RCT ontology specifications are obtained from: Trial Reports Trial Databases - ClinicalTrials.gov, PDQ etc.

The ontology is created by dividing the task into Sub- Tasks and Methods. This recursive process is called Competency Decomposition.

RCT decomposition methods combined Generic Tasks and Competency Question.


RCT Schema

TRAIL

Population

Recruited Population

Excluded Population

Analyzed Population

Administrative Concept

Intervention -ARM

Outcome- Concept

…….

…….

188 - Frames601 - Slots

…….

…….


Matching Patient Records to Clinical Trials

Low participation in Clinical Trials is the major problem in Clinical and translational research area.

Matching the patient records to clinical trials is presently a manual procedure and its tedious.

Need a Semantic Bridge between Clinical Ontologies (SNOMED CT, etc ..) and raw patient data for retrieving matching patient records, clinical guidelines and clinical decision support systems ( CDSS).


Technical Challenges

Challenges to be faced during real time scenario:

Knowledge Engineering.

Scalability

Noisy or Incomplete Data

Knowledge Engineering

Clinical Ontology has the concept “Drug”, which describe various active composition of the various

drugs. However, patient record contains name of vendor-specific drugs list.


Clinical Ontology describe the cause of the disorder. The patient records only specify the presence or absence of the disorder and where was the clinical test conducted.

Scalability

The size of the knowledge base and the patient data are very large. Can the reasoner handle such massive data.

Noisy or Incomplete Data

Clinical data is very inconsistent. But logical reasoners acts on this data assuming it to be complete.


Architecture

Patient

Data

ABox

SNOMED-CT

TBox

Query

Ontology

Reasoner

Clinical Trials


Solutions to the Challenges Mapping Patient Data Terminology to SNOMED-CT

Using UMLS as intermediate target.

NLP mapping techniques

Manual Mapping Map the raw patient data to SNOMED-CT terminology.

Example: Cerner Drug: Lactulose Syrup 20G/30ml SNOMED-CT: administeredSubstance.

Allow user to specify which terms in the definition to be matched. 31BioMedical Informatics


Sample Examples of above architecture

Э associatedObservation MRSA

Э associatedObservation Pneumococcal Penumonia П Э hasSpecimanSource Blood Ц Sputum


Clinical Decision Support System

Clinical Decision Support Systems (CDSS) are interactive computer programs, which are designed to assist physicians and other health professionals with decision making tasks.

Components of CDSS: Knowledge Base Rule Based Engine Case Base Business Models


IF “ RULE 1” &“RULE 2” &“RULE 3” ….. “Rule n”

THEN “INTERVENTION 1 or Rule M”

IF p.getGender() = “male”& p.getAge()=34 & p.getBP() <140 & p.getInsulinLevel()<20

THEN “ Asthma Intervention Level 2”

Class Patinet HasGender “male” П hasAge “34” П hasBP MoreThan 140 П

hasInsulinLevel MoreThan 20


Nursing COmputer Decision Support

The goal of NCODES is to provide a decision support system for novice nurses.

Inference Engine

Expert/ KnowledgeSources

Rules

K E

Feed Back

Knowledge Utilization and Representation Knowledge Acquisition

Wireless LAN


User End Presentation Layer

Server SideServer

End


The communication between the server and the PALM’s is through wireless network.

High Speed wireless LAN and Handheld devices like PDA constitute NCodes hardware.

Trying to in cooperate semantic knowledge base besides the present database. The present database has rule and information for Respiratory system.

Use SPARQL queries to retrieve the knowledge from the ontology.


PREFIX URI: <http://www.owl-ontologies.com/Cardiology.owl#> PREFIX RDFS: <http://www.w3.org/2000/01/rdf-schema#> PREFIX OWL: <http://www.w3.org/2002/07/owl#> PREFIX XSD: <http://www.w3.org/2001/XMLSchema#> PREFIX RDF: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> SELECT DISTINCT ?x "WHERE{?x URI:Have_Symptoms }".

Summary• Ontology

– Definition and Descriptions.

– Example.

• Biomedical Ontology– Open Cyc

– WordNet

– GALEN

– SNOMED - CT

• Integration of Ontologies

• Application of Biomedical Ontology– Clinical Trials.

– OASIS: Integration Technique.

– Clinical Decision Support System.


?“ The important thing is not to stop questioning. Curiosity has its own reason for existing.”


Documents

C urrent status of O ntologies in B iomedical and C linical I nformatics. By Rishi Kanth Saripalle Biomedical Informatics, University of Connecticut, Storrs