Mining personalized medicine algorithms with surrogate algorithm tags

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.