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Usage of Semantic Web Technologies (Web 3.0) Aiming to Facilitate the Utilisation of Computerized Algorithmic Medicine in Clinical Practice [Med2 Bratsas V2]

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Health & Medicine

Published on October 30, 2008

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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 4/5 th , 2008, in Toronto, was uploaded on behalf of the presenter by the Medicine 2.0 team Do not miss the next Medicine 2.0 congress on 17/18th Sept 2009 ( www.medicine20congress.com ) Order Audio Recordings (mp3) of Medicine 2.0’08 presentations at http://www.medicine20congress.com/mp3.php

This slideshow, presented at Medicine 2.0’08 , Sept 4/5 th , 2008, in Toronto, was uploaded on behalf of the presenter by the Medicine 2.0 team

Do not miss the next Medicine 2.0 congress on 17/18th Sept 2009 ( www.medicine20congress.com )

Order Audio Recordings (mp3) of Medicine 2.0’08 presentations at http://www.medicine20congress.com/mp3.php

Charalampos Bratsas, Panagiotis Bamidis *, Evangelos Kaimakamis, Nicos Maglaveras Lab of Medical Informatics, Medical School Aristotle University of Thessaloniki Usage of Semantic Web Technologies (Web-3.0) Aiming to Facilitate the Utilisation of Computerized Algorithmic Medicine in Clinical Practice

Outline 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 C Bratsas, P Bamidis *, E Kaimakamis, N Maglaveras

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

Medical Computational Problems – Computerized 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 To define MCPs and their solutions different domains of knowledge are required Collaboration of different kind of scientists .

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 .

Conclusions 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

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

Conclusions 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. C Bratsas, P Bamidis *, E Kaimakamis, N Maglaveras John R Svirbely, Jan Vejvalka, M Sriram Iyengar, Charalampos Bratsas, Evangelos Kaimakamis,Nicos Maglaveras. Technological guidelines for integrating medical algorithms into healthcare systems

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.

Main reason -Solution Structure Framework to describe MCPs  Ontologies Structure and Education C Bratsas, P Bamidis *, E Kaimakamis, N Maglaveras Doctors, Mathematicians, Physics , etc Informatics

Scope - 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 C Bratsas, P Bamidis *, E Kaimakamis, N Maglaveras Scope: The initial development of semantic descriptions of Medical Computational Problems (MCPs) and the management of resulting knowledge.

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

MCP 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

The MCP Ontology is an OWL ontology model that manages MCPs and their solutions by means of organizing and visualizing their existing knowledge.

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 / )

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 / )

Adaptation of the classical Vector Space Model ( VSM ) in MCP Ontology based on which 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. The similarity between MCP semantic descriptions and the user questions is calculated. Cosine Similarity MCP Ontology – Efficient Search C Bratsas, P Bamidis *, E Kaimakamis, N Maglaveras

Adaptation of the classical Vector Space Model ( VSM ) in MCP Ontology based on which

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.

The similarity between MCP semantic descriptions and the user questions is calculated.

Cosine Similarity

MCP Ontology - Managing a certain medical case Dynamic semantic composition of a sequence of algorithms Using semantic rules, the links between different algorithms are created and used in the construction of a Finite State Machine ( FSM ) of algorithms . 1 st Set of Rules: Define the Possible Prerequisites Algorithms of an algorithmic solution. (Input/Output Variables) 2 nd Set of Rules: Define the Possible Related Algorithms of an algorithmic solution. (Output/Output Variables) 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)

Dynamic semantic composition of a sequence of algorithms

Using semantic rules, the links between different algorithms are created and used in the construction of a Finite State Machine ( FSM ) of algorithms .

1 st Set of Rules: Define the Possible Prerequisites Algorithms of an algorithmic solution. (Input/Output Variables)

2 nd Set of Rules: Define the Possible Related Algorithms of an algorithmic solution. (Output/Output Variables)

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)

KnowBaSICS-M Modular Architecture

KnowBaSICS-M Technical Architecture Diagram 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 C Bratsas, P Bamidis *, E Kaimakamis, N Maglaveras

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

Experimental evaluation - Goals 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 C Bratsas, P Bamidis *, E Kaimakamis, N Maglaveras

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

Evaluation Process C Bratsas, P Bamidis *, E Kaimakamis, N Maglaveras

Experimental Results Similarity Similarity C Bratsas, P Bamidis *, E Kaimakamis, N Maglaveras New MCPs Satisfy Answer

Experimental Results of Search Precision Recall harmonic mean C Bratsas, P Bamidis *, E Kaimakamis, N Maglaveras

Test Case 1. Search similar MCP: Treatment of massive pulmonary embolism2. Find Algorithmic Sequence to manage a specific case C Bratsas, P Bamidis *, E Kaimakamis, N Maglaveras

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) C Bratsas, P Bamidis *, E Kaimakamis, N Maglaveras

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)

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