Mining personalized medicine algorithms with surrogate algorithm tags

Chih Lin Chi, Peter J. Kos, Vincent A. Fusaro, Rimma Pivovarov, Prasad Patil, Peter J. Tonellato

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationIHI'10 - Proceedings of the 1st ACM International Health Informatics Symposium
Pages474-478
Number of pages5
DOIs
StatePublished - Dec 1 2010
Event1st ACM International Health Informatics Symposium, IHI'10 - Arlington, VA, United States
Duration: Nov 11 2010Nov 12 2010

Publication series

NameIHI'10 - Proceedings of the 1st ACM International Health Informatics Symposium

Other

Other1st ACM International Health Informatics Symposium, IHI'10
CountryUnited States
CityArlington, VA
Period11/11/1011/12/10

Keywords

  • algorithms
  • keyword search
  • optimization

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