Text Mining of Medical Records for Radiodiagnostic Decision-Making
@article{Claster2008TextMO, title={Text Mining of Medical Records for Radiodiagnostic Decision-Making}, author={William Claster and Subana Shanmuganathan and Nader Ghotbi}, journal={J. Comput.}, year={2008}, volume={3}, pages={1-6} }
The rapid growth of digitalized medical records presents new opportunities for mining terra bytes of data that may provide new information & knowledge. [] Key Method We employed Self Organizing Maps (SOM), an unsupervised neural network based text-mining technique for the analysis. This approach led to the identification of keywords with a significance value within the narratives of the medical records that could predict & thereby lower the number of unnecessary CT requests by clinicians. This is important…
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Figure 4 Tabbed version of C5.0 decision tree .1 indicates the predication of a necessary scan and 2 indicates the prediction of an unnecessary scan
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