@misc{oai:repo.qst.go.jp:00068494,
 author = {Ando, Yutaka and Tsukamoto, Nobuhiro and Kawaguchi, Osamu and Kasamatsu, Tomotaka and et.al and 安藤 裕 and 塚本 信宏 and 川口 修},
 month = {Dec},
 note = {Purpose:DICOM Structured Reporting has advantages, however, many existing reporting systems create and store in free-text format. In this study, we developed and evaluated the automated method for creating structured reports from free-text format. 
Methods:100 NM reports (internal data-set)of brain perfusion scintigraphy were analyzed and categorized into expression units using text-mining technology to create a dictionary. Using this dictionary, other 100 reports (external data-set)were analyzed for the accuracy. The results were also compared against manual categorization by physicians. 
Results:In internal data-set, 79.5% of sentences has matched, and in external data-set 62.2% of sentences has matched to convert into units.
Discussion:Matching rate can be increased by improving the dictionary. In addition, by
automatically creating DICOM structured reports, clinical information can be easily translated into other languages, transferred to other systems, and can be searched rapidly., RSNA'05 91th Scientific Assembly and Annual Meeting},
 title = {Creating DICOM Structured Reporting Object from Free Text Reports Using the Text Mining Method},
 year = {2005}
}