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@PHDTHESIS{Liem:50322,
author = {Liem, Steve Wei-Lung},
othercontributors = {Spitzer, Klaus},
title = {{S}emantische {M}odellierung für ein wissensbasiertes
{S}ystem in der {P}ädiatrie},
address = {Aachen},
publisher = {Publikationsserver der RWTH Aachen University},
reportid = {RWTH-CONV-112871},
pages = {II, 97 S. : Ill., graph. Darst.},
year = {2008},
note = {Aachen, Techn. Hochsch., Diss., 2008},
abstract = {This project aimed to create a basic framework for a
computer-based system, which enables physicians and medical
students to quickly and efficiently retrieve medical
knowledge. The knowledge base DataMed is designed to assist
the physician with finding diagnoses and making clinical
decisions in his or her daily routine. Query algorithms make
complex queries possible, which conventional medical
literature does not allow. Textbooks have a linear structure
and are ordered by chapters (diseases). They are principally
suitable to comprehensively present knowledge about a
disease. DataMed can also be used as a reference. Its
advantage over conventional textbooks lies in its capacity
to reach a possible diagnosis with the use of incomplete
information, e.g. a small set of symptoms. This is the
result of the semantic network, with which information about
different diseases is interlinked and is now accessible to
computer-aided processing. The main focus of this work lies
in the conception of the data model. The first step was the
selection of a suitable knowledge domain. The domain of
pediatric viral diseases was chosen, because it is
representative and manageable. Its diseases are
characterised by categories like etiology, symptoms or
therapy, and are therefore representative for many internal
diseases. In addition, there are intersections between the
symptoms of the diseases, which allows a comparative
approach with differential diagnoses. The modeling of
knowledge was carried out under the paradigm of object
orientation, weak typisation, and the semantic network. The
textbook information was analysed, and translated into a
semantic network. This consists of objects and links. An
assertion like “measles cause fever” is broken down into
the objects “measles” and “fever”. These two objects
are then connected by a link with the name “has
symptom”. In order to produce a valid semantic network,
the model was based on a well-established nomenclature, the
SNOMED. The hierarchy of the SNOMED ensures consistency and
validity in case of future expansions. The current DataMed
model consists of 549 concepts, 1041 associations, 183
container objects and 214 SNOMED-concepts. The conception of
the data model was initially done independent from the
computer. Modeling steps alternated with evaluation steps
even before implementation. Improvements and “best
practices” in challenging cases (e.g. symptoms with
characteristic) were successfully integrated into the data
model. It is object oriented and has a high granularity.
Objects in the model are kept as simple as possible and
follow the principle of weak typisation. Most information
about the objects is inherent in the semantic network,
consisting of objects and associations. After the model was
consistent enough, it was digitised with a UML-editor
(Poseidon). With an import module, the UML model was mapped
into a object oriented databas (FastObjects). The quality of
the data model was then evaluated in example sessions and a
comparative analysis of original text and data model.
Currently, the model only contains a small sample of medical
knowledge. However, the model represents a stable and
efficient basis for larger medical knowledge domains. It is
consistent and capable of incorporating future expansions in
form and content. The modeling approach is, as textual
analysis has shown, not capable of entirely mapping medical
knowledge. Medical texts rely heavily on impicit information
hidden in sentence structure of natural language. The ratio
of modeled information can be increased through extensions.
The abstract nature of the object oriented model can be
complemented through integration of full texts and
additional multi-media elements (images, audio, and video).
Computer-based knowledge processing is quickly gaining
importance in the field of medicine. It was the objective of
this work to present criteria and solutions, which can be
used to translate a conventional medical text into an
abstract data model, which is then integrated into a
concrete medical knowledge base for clinical use. It shows
the feasibility of correctly and didactically representing
medical knowledge for a computer-based system. With DataMed,
problems and their solutions were identified, which resulted
from the dilemma of translation between natural language and
machine-readable information.},
keywords = {Semantisches Netz (SWD) / Objektorientierung (SWD) /
Semantische Modellierung (SWD) / Wissensrepräsentation
(SWD) / SNOMED (SWD) / Kinderheilkunde (SWD) /
Wissensbasiertes System (SWD)},
cin = {526500-2},
ddc = {610},
cid = {$I:(DE-82)526500-2_20140620$},
typ = {PUB:(DE-HGF)11},
urn = {urn:nbn:de:hbz:82-opus-25289},
url = {https://publications.rwth-aachen.de/record/50322},
}
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