The Potential for Applying Knowledge Base System for Diagnosis of Acute Respiratory Tract Infections
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Date
2010-05-11
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Addis Ababa University
Abstract
Knowledge base systems exercise information technology to acquire and utilize combined
human expertise. The technology can be very useful to institutions with clear objectives,
rules and problems to provide consistent answers for repetitive decision-making, processes
and tasks.
Knowledge base systems should be adopted and updated periodically to cater for the new
discoveries, and to enhance benefits by addressing the new changes in the clinical diagnostic
activities.
This research was done to preserve human expert level knowledge on the diagnosis of acute
respiratory tract infections so that to make available such expert-knowledge for diagnostic
activities.
The system, also, could be useful especially in the medical environment where knowledge
experts are few, often in scarcity and often soon retire before their expertise is documented.
Facts that constituted the global criteria for the knowledgebase were gathered from expert
physicians, pharmacists and nurses at the hospital of Dagmawi-minilik and Meshualekia
middle-level clinic, Addis Ababa, review of guidelines, manuals, journals of respiratory
infections, and online resources.
The system uses backward chaining with inference network and decision trees modeling
structures basing on facts to draw logical conclusions from the initial states to the final states
using respiratory diagnostic functions.
For the prototype development, Prolog programming language has been used. The
performance of the prototype system is evaluated on qualitative bases. The result is
encouraging to design a practical KBS for ARTI diagnosis.
Lastly, further studies should be done in artificial intelligence to solve the problem of rare
expertise in the diagnosis of respiratory infections.
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Keywords
Potential, Applying Knowledge, Base System, Diagnosis, Acute Respiratory, Tract Infections