ISSN : 1796-203X
Volume : 3    Issue : 1    Date : January 2008

Text Mining of Medical Records for Radiodiagnostic Decision-Making
William Claster, Subana Shanmuganathan, and Nader Ghotbi
Page(s): 1-6
Full Text:
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The rapid growth of digitalized medical records presents new opportunities for mining terra bytes of
data that may provide new information & knowledge. The knowledge discovered as such could
assist medical practitioners in a myriad of ways, for example in selecting the optimal diagnostic tool
from among numerous possible choices. We analyzed the radiology department records of children
who had undergone a CT scan procedure at Nagasaki University Hospital in the year 2004. 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 because, in spite of the valuable
diagnostic capacity of such procedures, the overuse of medical radiation does pose significant
health risks and staggering cost especially with regard to children.

Index Terms
medical informatics, text-mining, datamining, SOM, Kohonen Networks, Neural Networks