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  •   Open Research
  • AUT Faculties
  • Faculty of Design and Creative Technologies (Te Ara Auaha)
  • School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau
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Text classification for medical informatics: a comparison of models for data mining radiological medical records

Claster, WB; Shanmuganathan, S; Ghotbi, N; Sallis, PJ
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Text Classification for Medical informatics William Claster1.pdf (518.0Kb)
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http://hdl.handle.net/10292/6192
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Abstract
In this study we analyze 1024 free text digital records from pediatric patients who underwent CT scanning. The free text reports are from the digital records of patients who underwent CT scanning in a one-year period in 2004 at the Nagasaki University Medical Hospital in Japan. We use text mining algorithms to model the records. Each scan was evaluated by an expert in the field and classified as to whether the CT scan was necessary or not. A model was built that predicts this classification. The results show that models developed on raw text could contribute significantly to the physician’s decision to order a CT scan. Practically this is important because radiation at levels ordinarily used for CT scanning may pose significant health risks especially to children and thus the modeling of unnecessary scanning may lead to less exposure to radiation.
Keywords
Text Mining; Radiology; Bag-of-words; Vectorization; Vector Space Model
Date
2011
Source
Asia Pacific World, vol.2(1), pp.121 - 137
Item Type
Journal Article
Publisher
International Association for Asia Pacific Studies
DOI
10.316/apw.2011020107
Publisher's Version
http://dx.doi.org/10.3167/apw.2011020107
Rights Statement
This is a post-peer-review, pre-copyedited version of an article published in Asia Pacific World. The definitive publisher-authenticated version [see citation] is available online at: [see publisher's version].

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