الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
The impor
estimated simply because there seems to be a continuing advancement in the
complexity and severity of many diagnosed medical maladies. Today, even
the slightest inclination that there are symptoms, however minor, are
already treated to a barrage of diagnostic medical steps to try to determine if
there is an underlying problem that needs a more targeted medical solution.
Doctors go to great lengths to try to discover the potential causes behind
each symptom so that patients are always aware of their medical conditions
at any given time.
The medical field is the scientific discipline that deals with finding
cure for every conceivable type of illness and disease so this work uses case
based reasoning in medical field to help doctors diagnose diseases, to find
the appropriate treatment for the patient and to analyze causes and/or
treatments.
Medical Diagnoses Decision Support system have been proposed to
improve the quality of health care services. Knowledge-based systems,
compared to conventional data-base systems, are talented to support
medical diagnoses to be more accurate and efficient. However, knowledge
acquisition is usually a bottleneck in the process of developing such
systems. In machine learning, ambiguities rise when the machine tries to
understand human language generating uncertainty in the inferencing
process; the uncertainty is elucidated by using Semantic technology. One
possibility for acquiring medical knowledge, particularly tacit knowledge, is
to use data or historical cases in both syntactic and semantic ways. A case
III
based reasoning (CBR) system is a combination of processes and
previous experiences. This thesis proposes a semantic medical CBR, called
MedSDrive, as a decision support system that drives medical personal to
take better decisions regarding health services from diagnosing to curing.
The semantic reasoning mechanism allows deeper understanding of the
medical knowledge for more accurate selection and matching of prediagnosed
and verified cases and help in fixing most of the traditional CBR
problems and limitations. The goal is to retrieve the most suitable medicines
for new cases (patients) depending on an ontology based structure for
diseases which is adapted automatically to cover all current and future
diseases. The ontology amendment is an enhancement to CBR retrieval and
update phases.
In this work, we examine two object-oriented ontology based CBR
frameworks jCOLIBRI and myCBR to compare the similarity output for
same input query.
During the implementation of the MedSDrive system using jCOLIBRI
we found average similarity is higher than average similarity when using
MedSDrive system with myCBR which give 99.3% and 93.8 respectively.