  The aim of medical knowledge representation is to capture the detailed domain
knowledge in a clinically efficient manner and to offer a reliable resolution
with the acquired knowledge. The knowledge base to be used by a medical expert
system should allow incremental growth with inclusion of updated knowledge over
the time. As knowledge are gathered from a variety of knowledge sources by
different knowledge engineers, the problem of redundancy is an important
concern here due to increased processing time of knowledge and occupancy of
large computational storage to accommodate all the gathered knowledge. Also
there may exist many inconsistent knowledge in the knowledge base. In this
paper, we have proposed a rough set based lattice structure for knowledge
representation in medical expert systems which overcomes the problem of
redundancy and inconsistency in knowledge and offers computational efficiency
with respect to both time and space. We have also generated an optimal set of
decision rules that would be used directly by the inference engine. The
reliability of each rule has been measured using a new metric called
credibility factor, and the certainty and coverage factors of a decision rule
have been re-defined. With a set of decisions rules arranged in descending
order according to their reliability measures, the medical expert system will
consider the highly reliable and certain rules at first, then it would search
for the possible and uncertain rules at later stage, if recommended by
physicians. The proposed knowledge representation technique has been
illustrated using an example from the domain of low back pain. The proposed
scheme ensures completeness, consistency, integrity, non-redundancy, and ease
of access.
