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首页> 外文期刊>PLoS One >Similarity measures and attribute selection for case-based reasoning in transcatheter aortic valve implantation
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Similarity measures and attribute selection for case-based reasoning in transcatheter aortic valve implantation

机译：经齿轮机主动脉瓣植入基于案例推理的相似度测量和属性选择

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In a clinical decision support system, the purpose of case-based reasoning is to help clinicians make convenient decisions for diagnoses or interventional gestures. Past experience, which is represented by a case-base of previous patients, is exploited to solve similar current problems using four steps—retrieve, reuse, revise, and retain. The proposed case-based reasoning has been focused on transcatheter aortic valve implantation to respond to clinical issues pertaining vascular access and prosthesis choices. The computation of a relevant similarity measure is an essential processing step employed to obtain a set of retrieved cases from a case-base. A hierarchical similarity measure that is based on a clinical decision tree is proposed to better integrate the clinical knowledge, especially in terms of case representation, case selection and attributes weighting. A case-base of 138 patients is used to evaluate the case-based reasoning performance, and retrieve- and reuse-based criteria have been considered. The sensitivity for the vascular access and the prosthesis choice is found to 0.88 and 0.94, respectively, with the use of the hierarchical similarity measure as opposed to 0.53 and 0.79 for the standard similarity measure. Ninety percent of the suggested solutions are correctly classified for the proposed metric when four cases are retrieved. Using a dedicated similarity measure, with relevant and weighted attributes selected through a clinical decision tree, the set of retrieved cases, and consequently, the decision suggested by the case-based reasoning are substantially improved over state-of-the-art similarity measures.
机译：在临床决策支持系统中，案例的推理的目的是帮助临床医生进行诊断或介入手势的方便决策。过去的经验是由先前患者的案例基础表示的，利用使用四个步骤检索，重用，修改和保留来解决类似的当前问题。所提出的基于案例的推理已经集中在经膜状管主动脉瓣植入上，以响应血管进入和假体选择的临床问题。相关相似度量的计算是用于从壳体基础获得一组检索的病例的基本处理步骤。提出基于临床决策树的分层相似度测量来更好地整合临床知识，尤其是在壳体表示，案例选择和属性加权方面。用于评估基于案例的推理性能的138名患者的案例底座，并考虑了基于检索和重用的标准。血管接入和假体选择的灵敏度分别在0.88和0.94中，使用分层相似度，而标准相似度测量相对于0.53和0.79。当检索四个案例时，百分之九十的建议解决方案被正确分类为所提出的指标。使用专用的相似度测量，通过临床决策树选择相关和加权属性，该组检索的情况以及由基于案例的推理建议的决定基本上改善了最先进的相似性措施。

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