The Potential for Applying Knowledge Base System for Diagnosis of Acute Respiratory Tract Infections
| dc.contributor.advisor | Beshah, Tibebe (Ato) | |
| dc.contributor.author | Demissie, Solomon | |
| dc.date.accessioned | 2021-04-01T11:09:37Z | |
| dc.date.accessioned | 2023-11-18T12:47:21Z | |
| dc.date.available | 2021-04-01T11:09:37Z | |
| dc.date.available | 2023-11-18T12:47:21Z | |
| dc.date.issued | 2010-05-11 | |
| dc.description.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. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/25904 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Potential | en_US |
| dc.subject | Applying Knowledge | en_US |
| dc.subject | Base System | en_US |
| dc.subject | Diagnosis | en_US |
| dc.subject | Acute Respiratory | en_US |
| dc.subject | Tract Infections | en_US |
| dc.title | The Potential for Applying Knowledge Base System for Diagnosis of Acute Respiratory Tract Infections | en_US |
| dc.type | Thesis | en_US |