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首页> 外文会议>IEEE International Conference on Bioinformatics and Biomedicine Workshops >A Clinical Outcome Evaluation Model with Local Sample Selection: a Study on Efficacy of Acupuncture for Cervical Spondylosis
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A Clinical Outcome Evaluation Model with Local Sample Selection: a Study on Efficacy of Acupuncture for Cervical Spondylosis

机译：局部样品选择的临床结果评价模型：针刺针灸治疗颈椎病的疗效研究

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摘要

Local learning is a special learning framework that considers training samples located in a small region concentric of the query sample. In many applications the concept label of query sample can be evaluated effectively only by similar training samples, such as the famous K-nearest neighbors (KNN) classifier. The metric of locality or similarity is essential in local learning, which is often application oriented and implied in local geometry of input space. In this paper, we propose to apply local learning to the task of outcome assessment and evaluation on acupuncture for cervical spondylosis (CS) in a multi-center clinical trial. The analytic data are measures of three questionnaires which are recognized tools for subjective patient-reported outcomes (PROs) evaluation. We propose a similarity evaluation method based on both Euclidean distance and the therapy effect of recent records. A Non-Dominated Sort (NDS) based methods is applied to obtain a ranking of therapy effect. A WEKA implementation decision tree classifier is applied as the main learner in our work, with comparison to two base line methods. The result shows that the proposed local learning method dramatically outperforms the global version in both classification accuracy and computational costs.
机译：本地学习是一种特殊的学习框架，其考虑位于查询样本同心的小区域中的培训样本。在许多应用中，可以仅通过类似的训练样本来有效地评估查询样本的概念标签，例如着名的K-Collest邻居（KNN）分类器。局部性或相似性的度量在本地学习中是必不可少的，这通常是在输入空间的局部几何形状中取向和暗示的应用。在本文中，我们建议在多中心临床试验中对宫颈脊柱术（CS）针灸进行成果评估和评估的任务。分析数据是三个问卷调查的措施，该调查问卷是用于主观患者报告的结果（PROS）评估的公认工具。我们提出了一种基于欧几里德距离的相似性评价方法和最近记录的治疗效果。应用基于非统治的排序（NDS）的方法来获得治疗效果的排名。与两个基线方法相比，威卡实现决策树分类器应用于我们工作中的主要学习者。结果表明，所提出的本地学习方法以分类准确度和计算成本的显着优于全球版本。

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