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- W1594391086 abstract "We analysed electronic discharge summaries by text mining and demonstrated its potential to make an automatic diagnosis and classification. We extracted diagnosis related terminological information by morphological analysis from 4,784 discharge summaries of 14 representative diseases applying vector space model. We calculated the weights of each term for individual diseases and obtained each vector for 14 diseases by tf-idf and entropy methods. The index terms extracted from the discharge summary showed the features of the disease. In comparing tf-idf and entropy methods, the ranking of importance of the terms were different but the extracted terms were approximately the same. When applying these data to new cases, more than 96% of the correct diagnoses could be obtained by both methods. The classification by the clustering showed clear accordance with the code of ICD-9. These results suggest the possibility to classify diseases from medical documents automatically by using the text mining technique." @default.
- W1594391086 created "2016-06-24" @default.
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- W1594391086 date "2007-01-01" @default.
- W1594391086 modified "2023-09-24" @default.
- W1594391086 title "Discharge Summaries can be Diagnosed from Extracted Index Terms by Text Mining" @default.
- W1594391086 hasPublicationYear "2007" @default.
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