Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/3137
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Abstraction and Analysis of Clinical Guidance Trees
Authors: Turner, Kenneth J
Contact Email: kjt@cs.stir.ac.uk
Keywords: CGT (Clinical Guidance Tree)
Decision Tree
Formal Method
Healthcare
LOTOS (Language Of Temporal Ordering Specification)
Issue Date: Apr-2009
Publisher: Elsevier
Citation: Turner KJ (2009) Abstraction and Analysis of Clinical Guidance Trees, Journal of Biomedical Informatics, 42 (2), pp. 237-250.
Abstract: OBJECTIVES The aims of this work were: to define an abstract notation for interactive decision trees; to formally analyse exploration errors in such trees through automated translation to LOTOS (Language Of Temporal Ordering Specification): to generate tree implementations through automated translation for an existing tree viewer, and to demonstrate the approach on healthcare examples created by the CGT (Clinical Guidance Tree) project. APPROACH An abstract and machine-readable notation was developed for describing Clinical Guidance Trees: ADIT (Abstract Decision/Interactive Trees). A methodology has been designed for creating trees using ADIT. In particular, tree structure is separated from tree content. Tree structure and flow are designed and evaluated before committing to detailed content of the tree. Software tools have been created to translate ADIT tree descriptions into LOTOS and into CGT Viewer format. These representations support formal analysis and interactive exploration of decision trees. Through automated conversion of existing CGT trees, realistic healthcare applications have been used to validate the approach. RESULTS All key objectives of the work have been achieved. An abstract notation has been created for decision trees, and is supported by automated translation and analysis. Although healthcare applications have been the main focus to date, the approach is generic and of value in almost any domain where decision trees are useful.
Type: Journal Article
URI: http://hdl.handle.net/1893/3137
DOI Link: http://dx.doi.org/10.1016/j.jbi.2008.10.006
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author; you can only request a copy if you wish to use this work for your own research or private study.
Affiliation: Computing Science - CSM Dept

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