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Possibilistic Medical
Knowledge Representation
Model
MOHAMMAD ALSUN
LAURENT LECORNU
BASEL SOLAIMAN
Medical Decision Support Systems involve two main issues: medical knowledge representation and reasoning mechanisms adapted
to the considered representation model.In [27] R. Seising et al. defined the Medical knowledge as follows:
"The certain information about relationships that exist between symptoms and symptoms, symptoms and diagnoses, diagnoses and diagnoses and more complex
relationships of combinations of symptoms and diagnoses to a symptom or diagnosis are formalizations of
what is called medical knowledge." The term "symptom" is used for any information about
the patient's state health (anamnesis, signs, laboratory
test results, etc.).The major interest of using
the possibility theory comes from its capacity to represent different types of information (quantitative, qualitative, binary, etc.), as
well as different forms of information imperfections such as uncertainty, imprecision, ambiguity and incompleteness.Author's address: Institut Tel' ecom, T ' el' ecom Bretagne, ITI Dpt, '
Technopole Brest-Iroise, CS 83818, 29238 Brest Cedex 3, France.The proposed
representation, reasoning model and the obtained validation results
show a real interest in order to realize various goals of Medical Decision Support Systems such as classification, similarity estimation,
etc.Medical Decision Support Systems such as Knowledge
Based Systems, Case Based Reasoning Systems, Machine Learning Systems and Medical Data Mining Systems, have been constructed in order to reduce diagnosis
error risks, as well as to help physicians making high
quality and reliable medical decisions [4].In addition to the different types of information imperfections, the information
can be quantitative (numerical or binary) or qualitative
(nominal and ordinal) [17, 29].Starting from
the description, realized by an expert of the medical knowledge,
describing the relationship between symptoms and diagnoses, the
proposed approach consists on building a possibilistic model including the Medical Knowledge Base.These systems involve two main issues: the medical knowledge
representation and adapted reasoning mechanisms.Medical knowledge often suffers from different
forms of information imperfections (i.e., uncertainty,
imprecision, ambiguity, etc.).This paper proposes an approach to construct a new medical knowledge representation model,
based on the use of possibility theory.Moreover, the proposed approach
integrates several possibilistic reasoning mechanisms based on the
considered knowledge.The
medical knowledge, in general terms, has to be considered from two points of view: Expert Knowledge
related to the physician's description of different relationships between symptoms and diagnoses, symptoms
and symptoms, and diagnoses and diagnoses.Manuscript received May 30, 2011; released for publication September 6, 2011.Furthermore, diagnosis delivering is an error-prone task [3].Experts use their own experience of the medical
cases as well as references knowledge sources to define
the structure of the medical knowledge base.Thus, the heterogeneity
and imperfection of medical knowledge must be taken
into consideration while the construction of a Medical
Decision Support System.In other words, Medical Decision Support System has to be able to deal with heterogeneous and imperfect knowledge.The validation of the proposed approach is
then conducted using an Endoscopic Knowledge Base.Refereeing of this contribution was handled by Jean Dezert.Therefore, diagnosis delivering is
an extremely critical, although difficult task. Patient
Information is collected from each patient (patient data
collecting and structuring).The first is crucial in order
to establish Medical Knowledge Base, while the second
leads to establish the patient database (i.e., Medical Case
Base).INTRODUCTION
Physician is the direct responsible for health and
life of his patients.^
1557-6418/12/$17.00 ?c 2012 JAIF
1.
Possibilistic Medical
Knowledge Representation
Model
MOHAMMAD ALSUN
LAURENT LECORNU
BASEL SOLAIMAN
Medical Decision Support Systems involve two main issues: medical knowledge representation and reasoning mechanisms adapted
to the considered representation model. This paper proposes an approach to construct a new medical knowledge representation model,
based on the use of possibility theory. The major interest of using
the possibility theory comes from its capacity to represent different types of information (quantitative, qualitative, binary, etc.), as
well as different forms of information imperfections such as uncertainty, imprecision, ambiguity and incompleteness. Starting from
the description, realized by an expert of the medical knowledge,
describing the relationship between symptoms and diagnoses, the
proposed approach consists on building a possibilistic model including the Medical Knowledge Base. Moreover, the proposed approach
integrates several possibilistic reasoning mechanisms based on the
considered knowledge. The validation of the proposed approach is
then conducted using an Endoscopic Knowledge Base. The proposed
representation, reasoning model and the obtained validation results
show a real interest in order to realize various goals of Medical Decision Support Systems such as classification, similarity estimation,
etc.
Manuscript received May 30, 2011; released for publication September 6, 2011.
Refereeing of this contribution was handled by Jean Dezert.
Author’s address: Institut Tel´ ecom, T ´ el´ ecom Bretagne, ITI Dpt, ´
Technopole Brest-Iroise, CS 83818, 29238 Brest Cedex 3, France. ˆ
1557-6418/12/$17.00 °c 2012 JAIF
تلخيص النصوص العربية والإنجليزية اليا باستخدام الخوارزميات الإحصائية وترتيب وأهمية الجمل في النص
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