  The recent developments in the field of biomedicine have made large volumes
of biomedical literature available to the medical practitioners. Due to the
large size and lack of efficient searching strategies, medical practitioners
struggle to obtain necessary information available in the biomedical
literature. Moreover, the most sophisticated search engines of age are not
intelligent enough to interpret the clinicians' questions. These facts reflect
the urgent need of an information retrieval system that accepts the queries
from medical practitioners' in natural language and returns the answers quickly
and efficiently. In this paper, we present an implementation of a machine
intelligence based CLINIcal Question Answering system (CLINIQA) to answer
medical practitioner's questions. The system was rigorously evaluated on
different text mining algorithms and the best components for the system were
selected. The system makes use of Unified Medical Language System for semantic
analysis of both questions and medical documents. In addition, the system
employs supervised machine learning algorithms for classification of the
documents, identifying the focus of the question and answer selection.
Effective domain-specific heuristics are designed for answer ranking. The
performance evaluation on hundred clinical questions shows the effectiveness of
our approach.
