MedAT - Knowledge Engineering Group

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Nov 30, 2013 (3 years and 17 days ago)

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1

MedAT:

Medical Resources
Annotation Tool


Monika
Žáková
*
, Olga Štěpánková
*
, Taťána Maříková

*
Department of Cybernetics, CTU Prague

Institute of Biology and Medical Genetics
, Prague

z
akovm1
@
fel.cvut.cz
,
tana.marikova@lfmotol.cuni.cz



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Outline

1.
Motivation

2.
System Description

3.
Creating Annotation

4.
Additional Functionalities

5.
Knowledge Representation

6.
Ontologies


Task Ontologies


Domain Ontologies

7.
Results and Conclusion

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Motivation


Patients’ records represent a valuable source of
information


Records stored in semi
-
structured text files


For sharing and data mining format such as ontology or
relational database needed


Currently known methods for text mining not applicable,
since


Records heterogeneous


type of examination, personality of doctor


Abbreviations used (some non
-
standard)


Gazetteers not available in Czech


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Motivation II


Grant “Relational ML for analysis of biomedical data” of the
Czech national research program Information Society in
cooperation with the Institute of Biology and Medical
Genetics
, 2nd
Medical Faculty of Charles

University


Relational data mining using subgroup discovery
methodology


Need to transform data from text files into a form suitable
for relational data mining i.e. relational database and rules

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System overview

Ontologies

Medical record

Forms generator

Knowledge base

Relational
database

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System description


Creating semantic annotations of medical records


Based on Dynamic Narrative Authoring Tool


Modular architecture


Export to knowledge base in OCML, OWL


Export to relational database


Visualization


genealogical tree

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MedAT GUI

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Creating Annotations


Dynamically generated forms


A form


one major class in ontology


master table in
the database


E.g.
Patient, Examination


Adding abbreviations and aliases to the ontology


Filling of forms


Automatically by parsing


Drag and drop from records in text format


Manually in case OCR not effective

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Creating Annotations II

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Additional functionalities


Exploration of data stored in the relational database


Pre
-
defined SQL queries


knowledge of SQL not required


Writing queries directly in SQL



Visualization


Genealogy tree


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Knowledge representation


Core formalism


Apollet

Apollet


Frame
-
based formalism based on OCML


Formalism used by Apollo ontology editor => possibility to
use I/O modules of Apollo


Export to lisp available


Inference engine available


Disadvantage: rules very often just lisp functions

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Relational database


Tables of the relational database generated automatically
from the ontology


Semantic description of the database given by an ontology


Export done in a batch for a particular version of ontology
and knowledge base


Export intended for a data mining experiment


Currently PostgreSQL database used


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Ontologies

MedAT relies on ontologies on 2 levels:


Task ontologies


Describe structure of different medical records


Domain ontologies


Formalize knowledge about a specific domain
e.g. diseases, family relations, time points















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Task Ontologies


Developed on basis of procedures and structure of medical
records in cooperation with medical doctors


Hierarchy induced by part
-
whole relationship


OCML


slots with facets


OWL


hasPart, partOf
(W3C Working Draft)


Serve as basis for generating of forms and tables in
relational database

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Task Ontologies
-

Example


Classes
-

elements of medical
records e.g.
object of
examination, therapy


Slots


description of
composition of medical
records e.g. class
examination
has slots
date,
doctor, has_therapy

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Domain ontologies


Use of third party ontologies e.g. GALEN, Gene Ontology


Ontology of family relations


Need for rules e.g.
hasHalfBrother(x,y)








OWL


no standardized rule language (ORL)


OCML


lisp functions

))
,
(
)
,
(
(
))
,
(
)
,
(
(
:
,
)
(
)
,
(
b
y
hasParent
b
x
hasParent
a
y
hasParent
q
x
hasParent
b
a
y
male
y
x
ther
hasHalfBro







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Time ontology

Time ontology


Developed originally for historical narratives


Based on Allen’s algebra


Uncertain time points and intervals


Uncertain temporal position


Uncertain granularity


Extended to cover time events specific for medical domain


E.g. before surgery, during infancy


Available in OCML





















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Results


Easily transfer information from medical reports to
dynamically generated forms


Data from forms saved to a knowledge base and relational
database


Iterative extending of ontologies, adding aliases and
abbreviations


Tool currently being tested at the Institute of Biology and
Medical Genetics for patients with neurofibromatosis type 1

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Future work


Text mining methods for semi
-
automatic annotation


Tool for semantic search and retrieval of a relevant subset
of data and visualization of retrieved data


Use of annotated data along with information about
genotype for data mining using subgroup discovery
methodology

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Questions

Thank you for your attention


Questions???