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

Bamidis, P. et al.: Usage of Semantic Web Technologies (Web 3.0) Aiming to Facilitate the Utilisation of Computerized Algorithmic Medicine in Clinical Practice

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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 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

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 .

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

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

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.

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.

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 / )

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

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)

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

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

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 ) ,