Information Integration in HealthAgents

Madalina Croitoru

croitoru@lirmm.fr

Presentation Structure

HealthAgents: motivation and background

The HealthAgents Ontology: HaDo

Extensions – Representation: HaDom v.1

Extensions – Reasoning: HaDom v.2

Discussion

Participants

•MicroArt•Universitat Autònoma

de Barcelona

•Universitat de València•ITACA

•Pharma Quality Europe

•Katholieke Universiteit Leuven

•University of Edinburgh

•University of Birmingham

•University of Southampton

Objectives

SCIENTIFIC

Brain Tumour Classification

New Pattern Recognition Methods

New Candidate DB Checking

“Self-learning” Classifiers

“Trusted” Framework

Dissemination

TECHNOLOGICAL

Large d-DWH

Multi-Agent System

New Ontology

Auto-conversion of Tumour Data

Tumour Exchange Protocol

Classifiers & d-DWH Coupling

Data Mart Comparison & Analysis

Improved Classifiers

Enhanced Security

Why brain tumours?

Clinical importance Important cause of morbidity and mortality in adults

and children Few improvements in outcome New approaches to management needed via greater

understanding Amenable to techniques

Brain is amenable to MR studies Tissue available, surgery mainstay of treatment

Our solution

Architecture

Database M

appingMaps relational database schema to HealthAgents ontological schema

- D2R (SPARQL to RDBMS)

Program

matic A

PIAbstracts underlying

database interaction from agent architecture

- SPARQL vs RDQL

Business M

ethods

Security and T

rust

Agent Layer (M

essage Passing Interface)

Main control flow of agent.

- Receive classifiers, retrieve data

Access control, marshalling of data, track out going data, evaluation of reputation and trust of agents

Communications

- abstracted from rest of agent to allow flexibility in the underlying framework

Relational Database

Semantic Description

Description of what the agent holds and what it is able to do- Can it classify?- Does it hold data?

Architecture

Database M

apping

Program

matic A

PI

Business M

ethods

Security and T

rust

Agent Layer (M

essage Passing Interface)

Semantic Description

Relational Database

Architecture

Architecture

Achitecture

Achitecture

PROPOSITION: CLASSIFY CASE X [DATA ….]

Agents:ABCD

A

B

C

D

Achitecture

A

B

C

D

Agents:ABCD

Achitecture

A

B

C

D

Agents:ABCD

INFORMATION: CLASSIFICATION CASE X [Astrocytoma 3]

INFORMATION: CLASSIFICATION CASE X [Astrocytoma 3]

INFORMATION: CLASSIFICATION CASE X [Astrocytoma 2]

INFORMATION: Insufficient Access Rights

HealthAgents: ontology

Two extremes

We don’t want this

We don’t want this either

What an ontology is and is not

Tower of Babel

Ontology is to facilitate mutual understanding

SW extends such mutual understanding to “machinery” Machine readable Machine processableand, at the same time Human readable Human

understandable

早上好 !

Buenos Dias!

Good Morning!

Bonjour!

おはよう !

Guten Morgen!

Buona Mattina!

Bom Dia!

좋은 아침 !

Καλημέρα!

Rumours about ontologies

Ontologies overly publicised: “Ontology” is becoming a buzz word “Ontology” can “say” whatever one wants

to say “Ontology” means inference “Ontology” is the ultimate solution for

interoperability

Ontology as a solution

A common language/vocabulary/terminology for various participants Formalised in an unambiguous representation For software agents, human experts, patients

A “static” conceptualisation of the world “What is” rather than “How does” Allows reasoning which respects the translation of concepts

as sets of (possible) individuals Provides the underlying knowledge model for other types of

reasoning, e.g. Rule Based, Case Based, Bayesian Network, etc.

Temporal stamp can be used to introduce dynamic flavours A standard template for information interchange

Why OWL (Web Ontology Language)?

Reasoning capability subsumption relationship

Relies on necessary and sufficient definition of concepts Good for maintaining a consistent ontology

W3C standard Good support: existing systems and tools Good compatibility:

many ontologies are developed in owl or will be translated into owl

Good extensibility

HaDO Version 0.1

A simple HealthAgents domain ontology is developed Different imaging modalities WHO (world health organisation) classification Necessary information for creating a new case Information to be passed to other agents and

humans Place-holder for new information in follow-up

revisions

Introducing HaDOM

MRS: as the result of UAB&ITACA visit late 2006 Follow the HealthAgents database schema Need to adapt according to the HA DB schema

MRI, MicroArray, and other examination modules For completeness No immediate use in HealthAgents?

Histopathology Follow the WHO classification

Patient information Minimum set for HealthAgents Optimal amount need to be investigated

Working with HaDom 1/2

Mapping between the domain ontology and current HealthAgents DB schema D2RQ mapping script (RR) Driving querying HA-DB via D2RQ engine Progressing well DB schema specific

D2RQ

DB1

DB3

DB2

DBn

Onto

Working with HaDom 2/2

Using HaDom as the common language Domain ontology contains only “invariants”

Only static domain knowledge Dynamic and correlation knowledge can be

built on top of HaDom Inference rules can be built on top of domain

ontology First approach: using Conceptual Graphs

Validation visits to Birmingham will be arranged in August

DICOMpatibility easiest solution: make concept and property names

“dicomised” Inference rules

Using HaDom for invariants Describing dynamic knowledge with HaDom-vocabulary

Bayesian Network with HaDom compatible inputs and outputs Role based access control with HaDom concepts as roles Etc.

What is missing: extensions

HADOM ontology – March 2008

The ontology provides the terminology for interoperability: WHO tumour classes (2002, 2007) HealthAgents core concepts:

MRS, MRI, HRMAS terminology Patient, Clinical_Intervention,

Medical_Control etc. Security concepts

haCore: for basic concept hierarchies and necessary concepts

classifier: gathers input and output parameters for HA classifiers

WHO 2007 Classification

WHO 2002 Classification

2007

2002

Conclusions

Presented our efforts in building the HealthAgents Ontology : HaDom Knowledge Acquisition Implementation Evaluation Revision Knowledge Acquisition Implementation Evaluation…

Lessons learnt…