GHOTBI Nader    Department   Ritsumeikan Asia Pacific University  College of Asia Pacific Studies    Position   Professor
Language	English
Publication Date	2011/08
Type	Bulletin of university, Research institution
Peer Review	Peer reviewed
Title	Hybrid Approach to Data Mining Radiological Medical Records
Contribution Type	Joint Work
Journal	Geoinformatics Research Center Publications
Author and coauthor	William Claster, Subana Shanmuganathan and Nader Ghotbi
Details	Here we develop a hybrid methodology for the analysis of medical records that consist of clinicians’ notes (requesting for a scan) using statistical tools and Kohonen’s self-organizing map (SOM) text mining techniques to look for heretofore invisible patterns to save patients from unwarranted scans. The medical data derive from patients’ radiology department records where CT (Computed Tomography) scanning was used as part of a diagnostic exploration. The records are from the digital records of about 700 pediatric patients who underwent CT scanning (single and multiple) through a one-year period in 2004 at the Nagasaki University Medical Hospital in Japan. This approach led to a model based on SOM clusters and statistical analysis which allow for the prediction of when a particular medical screening procedure may be unnecessary. The procedure involves CT scans of patients. Note: This is important because radiation at levels ordinarily used for CT scanning may pose significant health risks especially to children.