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Real World Scenario: HealthAgents
Transcript of Real World Scenario: HealthAgents
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…