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Mining personalized medicine algorithms with surrogate algorithm tags

Published: 11 November 2010 Publication History

Abstract

This paper demonstrates a method to identify keyword strategies to facilitate the search for articles containing decision support and clinical algorithms represented in the text by complex unsearchable items such as decision tree figures, pseudo code, or mathematical formulae. We use a text mining approach to generate 'Surrogate Algorithm Tags' (SRATs), i.e., keyword combinations highly associated with articles containing the algorithms of interest. In this project, we obtain an initial SRAT set from analyzing abstracts of publications available in PubMed with known warfarin dosing algorithms, gradually refine the SRATs by iterative optimization to improve precision or recall of the search, and then apply cut-off thresholds to terminate the optimization process and obtain optimal SRATs.

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cover image ACM Other conferences
IHI '10: Proceedings of the 1st ACM International Health Informatics Symposium
November 2010
886 pages
ISBN:9781450300308
DOI:10.1145/1882992
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 11 November 2010

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Author Tags

  1. algorithms
  2. keyword search
  3. optimization

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IHI '10
IHI '10: ACM International Health Informatics Symposium
November 11 - 12, 2010
Virginia, Arlington, USA

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