Literature DB >> 18694198

Using data mining tools to discover novel clinical laboratory test batteries.

Jennifer Santangelo1, Patrick Rogers, Jason Buskirk, Hagop S Mekhjian, Jianhua Liu, Jyoti Kamal.   

Abstract

Using statistical analysis and data mining tools, we examined possible associations among clinical laboratory orders placed at the Ohio State University Medical Center between January and October of 2006. Upon applying the Frequent Itemset data mining technique to this data set, the results indicated that, while the most frequently ordered battery of tests was not associated with others, some highly associated orders may be good candidates to comprise new test batteries.

Mesh:

Year:  2007        PMID: 18694198

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN: 1559-4076


  1 in total

1.  A recommendation algorithm for automating corollary order generation.

Authors:  Jeffrey Klann; Gunther Schadow; J M McCoy
Journal:  AMIA Annu Symp Proc       Date:  2009-11-14
  1 in total

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