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Bamidis, P. et al.:Usage of Semantic Web Technologies (Web 3.0) Aiming to Facilitate the Utilisation of Computerized Algorithmic Medicine in Clinical Practice
• This slideshow, presented at Medicine 2.0’08, Sept 4/5th, 2008, in Toronto, was uploaded on behalf of the presenter by the Medicine 2.0 team
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Charalampos Bratsas, Panagiotis Bamidis *, Evangelos Kaimakamis, Nicos Maglaveras
Lab of Medical Informatics, Medical School
Aristotle University of Thessaloniki
Usage of Semantic Web Usage of Semantic Web Technologies (Web-3.0) Aiming to Technologies (Web-3.0) Aiming to
Facilitate the Facilitate the UtilisationUtilisation of of Computerized Algorithmic Computerized Algorithmic
Medicine in Clinical PracticeMedicine in Clinical Practice
OutlineOutline
Definition of Medical Computational Problems and the benefits of use algorithms in Medicine
Why algorithmic medicine doesn't used? What is the main problem?
Scope – Solutions Ontologies as a structure framework of MCPs Methods and Web-System architecture (KnowBaSICS-
M) Experimental evaluation and test case Future research
3C Bratsas, P Bamidis*, E Kaimakamis, N Maglaveras
MedicalMedical Computational ProblemsComputational Problems – – Computerized Algorithmic SolutionsComputerized Algorithmic Solutions
Medical Computational Problems MCPs: Medical problems, the solution of which deals with mathematical or statistical models, signal or image processing and estimation of corresponding parameters.
C Bratsas, P Bamidis*, E Kaimakamis, N Maglaveras
Screen a patient for sleep apnea using the multivariable apnea risk index
Medical ProblemClinical Algorithm
Implementation
Medical Computational Problem
u2
x3
x4
u1
x2
x1
f(x1...xn)
A11
A11
A11
A11
A11
A11
Maislin et.alscore for predicting sleep apnea in patients
To define MCPs and their solutions diffe
rent domains
of knowledge are required
Collaboration of different kind of scientists.
Conclusions of MIE 2006 WorkshopConclusions of MIE 2006 Workshop
There are tens of thousands of algorithms. They are not widely incorporated into routine care. We believe that healthcare would be better if they
were. Ontology support for Algorithmic Medicine
John R Svirbely, Jan Vejvalka, M Sriram Iyengar, Charalampos Bratsas, Evangelos Kaimakamis, Nicos Maglaveras.
Technological guidelines for integrating medical algorithms into healthcare systems
C Bratsas, P Bamidis*, E Kaimakamis, N Maglaveras
C Bratsas, P Bamidis*, E Kaimakamis, N Maglaveras
Conclusions of MIE 2006 WorkshopConclusions of MIE 2006 Workshop
Why aren’t algorithms used? ◦ I don’t have the time.◦ I didn’t know there was one.◦ I don’t remember what it is.◦ I don’t have a software.◦ I don’t have the data I need.◦ I don’t know how to use it.
John R Svirbely, Jan Vejvalka, M Sriram Iyengar, Charalampos Bratsas, Evangelos Kaimakamis,Nicos Maglaveras.
Technological guidelines for integrating medical algorithms into healthcare systems
Main reason -Solution Main reason -Solution
Doctors, Doctors, Mathematicians, Mathematicians, PhysicsPhysics , etc, etc
InformaticInformaticss
Structure Framewor
k to describe MCPs Ontologie
s
Structure and EducationC Bratsas, P Bamidis*, E Kaimakamis, N Maglaveras
C Bratsas, P Bamidis*, E Kaimakamis, N Maglaveras
Scope - SolutionsScope - Solutions
Develop the semantic framework (MCP Ontology]) enclosing the required knowledge based on which the medical problem - algorithm - implementation are semantically described.
Develop knowledge retrieval methods, through ontological questions and the utilization of information retrieval methods inside the MCP Ontology.
Develop dynamic semantic composition of a sequence of algorithms managing a certain medical case
Scope:The initial development of semantic descriptions of Medical Computational Problems (MCPs) and the management of resulting knowledge.
MCP OntologyMCP Ontology
The MCP Ontology is an OWL ontology model that manages MCPs and their solutions by means of organizing and visualizing their existing knowledge.
C Bratsas, P Bamidis*, E Kaimakamis, N Maglaveras
MCP Ontology Model MCP Ontology Model
Ontologies :◦ Medical
Problem Ontology
◦ Medical Algorithm Ontology
◦ Implementation Ontology
◦ Users Ontology
Reuses or/and Adaptations:•BibTex Ontology to semantically describe the MCPs References (http://www.cs.toronto.edu/semanticweb/maponto/ontologies/BibTex.owl )
•UMLS Ontology to semantically describe the medical concepts. (Unified Medical Language System) (http://umlsks.nlm.nih.gov/kss)
•ConOnto Ontology to semantically describe the software and hardware of implemented algorithm (http://www.site.uottawa.ca/~mkhedr/Ontologies/ )
•Global Medical Device Nomenclature to semantically describe the medical devices (http://www.gmdnagency.com/) 10
Adaptation of the classical Vector Space
Model (VSM) in MCP Ontology based on which1. The MCP weighted vectors are created by the
implementation of the weights of the UMLS terms acting as the problem indexing terms in the MCP Ontology
tf factor: based on the frequency of occurrence of the instances of a keyword (UMLS concept) into MCPs natural language description
idf factory: based on frequency of occurrence of the instances of a keyword (UMLS concept) into MCP Ontology.
2. The similarity between MCP semantic descriptions and the user questions is calculated.
Cosine Similarity
MCP Ontology – MCP Ontology – Efficient SearchEfficient Search
C Bratsas, P Bamidis*, E Kaimakamis, N Maglaveras
MCP Ontology - Managing a MCP Ontology - Managing a certain medical casecertain medical case
◦ Dynamic semantic composition of a sequence of algorithms1.Using semantic rules, the links between different
algorithms are created and used in the construction of a Finite State Machine (FSM) of algorithms. 1st Set of Rules: Define the Possible Prerequisites
Algorithms of an algorithmic solution. (Input/Output Variables)
2nd Set of Rules: Define the Possible Related Algorithms of an algorithmic solution. (Output/Output Variables)
2.Description of a certain medical case via the MCP Ontology by a user constitutes the language of that case which is recognised by a FSM of algorithms with the final algorithm managing the case as the initial state and the algorithm of initiation by the user as the final state. Set of Rules: Define the Available Algorithmic Solutions for
a specific medical case (Pre-conditions are met)
Knowledge Insertion and Redefine
Module
MCP KB
Semantic MCP Repository
MCP Ontology Model
Encodes the MCP semantic model andprovides knowledge
acquisition
Query Formulator
Query Processor
Result Set Retrieval
Query Engine
Similarity Calulator
Vectors Constructor
Ontology VSM
Create Vectors fromInstances of the extractedTerms and calculates similarity metrics
Medical Concepts Extractor
Medical Terms Annotator
UMLS-KB
UMLS-based medical concepts extraction on the
user define query
Medical Case Management Planning
FSM Constructor
Algorithmic Solution Sequence Designer
Creates a FSM of available algorithms for specific
medical case Retrieve the algorithmic sequence that manages
the specific case
Performs Ontology based (SPARQL) knowledge retrievalon MCPS
User Interface
13
KnowBaSICS-M Modular Architecture KnowBaSICS-M Modular Architecture
KnowBaSICS-M Technical Architecture KnowBaSICS-M Technical Architecture DiagramDiagram
Code development was based on open-source development platforms and tools: (Protégé, Java, Jena, eclipse, Millstone)
The system consists of:◦ MCP Management Server◦ 2 Clients
Java Standalone Web Client
14C Bratsas, P Bamidis*, E Kaimakamis, N Maglaveras
Experimental evaluationExperimental evaluation - - GoalsGoals
To evaluate KnowBaSICS-M either for knowledge insertion or for knowledge retrieval in order to assess its usability.
To calculate the precision and recall features. To evaluate KnowBaSICS-M to manage
specific cases by dynamically semantic composite algorithmic sequences
15C Bratsas, P Bamidis*, E Kaimakamis, N Maglaveras
Evaluation ProcessEvaluation Process
Process Layer
Result Layer
DecisionLayer
Criteria Layer
Phase 1 Phase 2 Phase 3 Phase 4
1. Web Sites with MCPs 2.Well, no structure – no
semantic description3. MCP Categories
4. Availability of algorithmic implementations
232 Semantic descriptions of MCPs though
KnowBaSICS-M
Web MedAl Project
Initial MCP KB
1. Know about MCPs2. Familiar with the MedAl
projec
Users Defined in MCP KB
4 Users Specialist in the fields of
cardiology and pulmonary medicine
Define Users as Knowledge Authors in MCP Ontology
1. Express their queries in a very descriptive,natural language
2. No instructions or training concerning the keywords
selection
Users instructions
Result sets of ranking MCPs
A total of 68 clinical
Questions were addressed by the
physicians
Evaluation Process
1. Users manual marks relevant MCPs residing in the MCP KB corresponded to their clinical
questions.2. Users compare the manual marking of the MCPs and the
results obtained from the system3. Create new MCPs instances -
New Knowledge
Estimation of precision and recall
futures
New MCPs in KB
Result assessment
16C Bratsas, P Bamidis*, E Kaimakamis, N Maglaveras
Experimental Results of Search Experimental Results of Search
18
Precision
Recall
harmonic mean
C Bratsas, P Bamidis*, E Kaimakamis, N Maglaveras
Test Case 1. Search similar MCP: Treatment of Test Case 1. Search similar MCP: Treatment of massive pulmonary embolism2. Find Algorithmimassive pulmonary embolism2. Find Algorithmic Sequencec Sequence
to manage a specific case to manage a specific case
19C Bratsas, P Bamidis*, E Kaimakamis, N Maglaveras
Future Research Future Research
Major technical challenge is the automated incorporation of the content located at existing repositories such as MedAl in the MCP KB (wrapper-mediation based)
An extension of KnowBaSICS-M is considered to support the automated identification of individualised algorithms that will be linked with Electronic Health Record (EHR) data (Archetype - OpenEHR),
High quality medical education (Problem Based Learning & Case Based Learning - HealthCare LOM -SCORM)
Semantic Wiki about algorithmic medicine◦ combination of web-2.0 and Semantic Web (e.g. wiki
professional)
20C Bratsas, P Bamidis*, E Kaimakamis, N Maglaveras