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首页> 外文期刊>Journal of biomedical informatics. >Abstraction and analysis of clinical guidance trees.
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Abstraction and analysis of clinical guidance trees.

机译：临床指导树的抽象和分析。

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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: Ad/it (abstract decision/interactive trees). A methodology has been designed for creating trees using Ad/it. 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 Ad/it 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.
机译：目标：这项工作的目的是：为交互式决策树定义一个抽象符号;通过自动翻译成Lotos（时间顺序规范的语言）来正式分析此类树中的勘探错误;通过自动翻译为现有的树查看器生成树实现，并演示CGT（临床​​指导树）项目创建的医疗示例方法。方法：开发了一种抽象且机器可读的符号来描述临床指导树：Ad / it（抽象决策/交互树）。设计了一种使用Ad / it创建树的方法。特别地，树结构与树内容分开。在致力于树的详细内容之前，会设计和评估树的结构和流程。已经创建了软件工具来将Ad / it树描述转换为Lotos和CGT Viewer格式。这些表示支持形式分析和决策树的交互式探索。通过自动转换现有CGT树，实际的医疗保健应用已用于验证该方法。结果：工作的所有关键目标均已实现。已经为决策树创建了一个抽象符号，并得到自动翻译和分析的支持。尽管迄今为止，医疗保健应用一直是主要关注点，但是该方法是通用的，并且在决策树有用的几乎任何领域都具有价值。

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