Abstract: Data mining has been used very frequently to extract
hidden information from large databases. This paper suggests the use
of decision trees for continuously extracting the clinical reasoning in
the form of medical expert-s actions that is inherent in large number
of EMRs (Electronic Medical records). In this way the extracted data
could be used to teach students of oral medicine a number of orderly
processes for dealing with patients who represent with different
problems within the practice context over time.
Abstract: Expert systems are used extensively in many domains.
This paper discusses the use of medical expert systems in Pakistan.
Countries all over the world pay special attention on health facilities.
A country like Pakistan faces a lot of trouble in health sector.
Several attempts have been made in Pakistan to improve the health
conditions of the people but the situation is still not encouraging.
There is a shortage of doctors and other trained personnel in
Pakistan. Expert systems can play a vital role in such cases where the
medical expert is not readily available. The purpose of this paper is
to analyze the role that such systems can play in improving the health
conditions of the people in Pakistan.
Abstract: Fast retrieval of data has been a need of user in any
database application. This paper introduces a buffer based query
optimization technique in which queries are assigned weights
according to their number of execution in a query bank. These
queries and their optimized executed plans are loaded into the buffer
at the start of the database application. For every query the system
searches for a match in the buffer and executes the plan without
creating new plans.
Abstract: The vast amount of information hidden in huge
databases has created tremendous interests in the field of data
mining. This paper examines the possibility of using data clustering
techniques in oral medicine to identify functional relationships
between different attributes and classification of similar patient
examinations. Commonly used data clustering algorithms have been
reviewed and as a result several interesting results have been
gathered.