An inductive method for automatic generation of referring physician prefetch rules for PACS.

J Digit Imaging

Graduate School of Medicine, Course of Health Sciences, Faculty of Medicine, Osaka University, 1-7 Yamadaoka, Suita, Osaka 565-0871, Japan.

Published: December 2002

AI Article Synopsis

  • A method was developed to prefetch medical images in a hospital's PACS by creating rules for selecting relevant examinations based on practical data.
  • The approach utilized a decision tree algorithm and was tested using extensive data from Osaka University Hospital, covering over 58,000 consultation reservations and 600,000 examination histories.
  • The results showed a selection sensitivity of around 0.8 for correctly identifying consultations where images were requested, and a specificity of about 0.7 for accurately excluding those without requests.

Article Abstract

To prefetch images in a hospital-wide picture archiving and communication system (PACS), a rule must be devised to permit accurate selection of examinations in which a patient's images are stored. We developed an inductive method to compose prefetch rules from practical data which were obtained in a hospital using a decision tree algorithm. Our methods were evaluated on data acquired in Osaka University Hospital for one month. The data collected consisted of 58,617 cases of consultation reservations, 643,797 examination histories of patients, and 323,993 records of image requests in PACS. Four parameters indicating whether the images of the patient were requested or not for each consultation reservation were derived from the database. As a result, the successful selection sensitivity for consultations in which images were requested was approximately 0.8, and the specificity for excluding consultations accurately where images were not requested was approximately 0.7.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3611617PMC
http://dx.doi.org/10.1007/s10278-002-0033-4DOI Listing

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