تهدف بحوث الذكاء الصنعي إلى بناء آلة تفكر, و إلى تحسين فهمنا للذكاء. و الهدف الأسمى في هذا الحقل هو بناء آلة تستطيع أن تقلد الإمكانات العقلية لدى الإنسان أو تتفوق عليها, من حيث المحاكمة و الفهم و التخيل و الإدراك و الإبداع والعواطف.
This paper presents a new solution that allows the doctors to know drug interactions,
considering other affecting factors such as the patient's age, weight, physiological and
pathological condition. This solution is characterized by being increment
al, not only by
enriching the database with drug interactions information, but also by its ability to conclude
other interactions through a built-in expert system. The system concludes drugs interactions
based on its active substrates and the potential interactions between them or between the drugs
families. The system serves in three ways; it determines whether the patient illness is possibly
due to the medications he is on. It alerts the doctor to the interaction of the newly prescribed
medication with the patient’s medications, and to its influence on the patient’s physiological or
pathological condition. Besides, it suggests alternative drugs when needed. The solution offers
additional services such as binding between the brand name and the generic drug, and between
drugs and diseases.
النظم الخبيرة
التداخلات الدوائية
الحالة الفيزيولوجية
الحالة المرضية
الآثار الجانبية
العائلة الدوائية
المادة الفعالة
الاسم العلمي
الاسم التجاري
الاستطبابات
Expert Systems
Drugs Interactions
Active Substrate
Medical Group
Genuine Name
Trade Name
physiological condition
pathological condition
Side Effects
Indications
المزيد..
In Artificial Intelligence field, Knowledge Engineering phase is considered
the most crucial phase of the development life cycle of the Knowledge Base
Systems [1]. In fact, Formal Logic in general and Modus Ponens specifically has
been the dominan
t tools for structuring this knowledge [3]. This led for forming
a gap between the knowledge area and the information area, which depends
structurally on the Set Theory in general and on the Relational Algebra in
particular [1]. Thus, trying to introduce a bridge to pass this gap in structuring
and treating knowledge, we have conducted a new knowledge representation
model that depends structurally on (Classical and Fuzzy) Set Theory. Then we
used it as the base for conducting an inference model that attempt, using a set
of algebraic operations and by going through a series of stages, to reach a
solution of the problem under study, in a manner very close to the one that
humans usually use in treating their knowledge, taking into consideration the
speed and accuracy as much as the problem allows.
