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This book addresses the problem of text classification. The main
motivation is the accurate classification of medical text reports.
Such documents contain important information about patients,
disease progression and management, but are difficult to analyse
and query with conventional techniques due to their unstructured
nature. We show how these medical reports can be classified
automatically with a high degree of accuracy. A novel method is
developed for accurate classification of medical reports. The
method uses clustering as a pre-processing step to improve the
final classification accuracy. The work requires the investigation
of different methods for document representation, clustering and
classification. In addition, it requires the use of Natural
Language Processing tools. A new approach that requires minimal
labelling effort, is found to be an effective classification tool
for this task. Results show that the approach produces good
classification performance on a real-world medical problem.
Importantly, the addition of clustering features further improves
the accuracy of the final classifier. Results are cross-checked
using different medical classification tasks
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