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Tongue Image Database Construction Based on the Expert Opinions: Assessment for Individual Agreement and Methods for Expert Selection

机译:基于专家意见的舌头图像数据库建设:个人协议评估和专家选择方法

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摘要

This study aims at introducing a method for individual agreement evaluation to identify the discordant raters from the experts' group. We exclude those experts and decide the best experts selection method, so as to improve the reliability of the constructed tongue image database based on experts' opinions. Fifty experienced experts from the TCM diagnostic field all over China were invited to give ratings for 300 randomly selected tongue images. Gwet's AC1 (first-order agreement coefficient) was used to calculate the interrater and intrarater agreement. The optimization of the interrater agreement and the disagreement score were put forward to evaluate the external consistency for individual expert. The proposed method could successfully optimize the interrater agreement. By comparing three experts' selection methods, the interrater agreement was, respectively, increased from 0.53 [0.32-0.75] for original one to 0.64 [0.39-0.80] using method A (inclusion of experts whose intrarater agreement>0.6), 0.69 [0.63-0.81] using method B (inclusion of experts whose disagreement score=“0”), and 0.76 [0.67-0.83] using method C (inclusion of experts whose intrarater agreement>0.6& disagreement score=“0”). In this study, we provide an estimate of external consistency for individual expert, and the comprehensive consideration of both the internal consistency and the external consistency for each expert would be superior to either one in the tongue image construction based on expert opinions.
机译:本研究旨在介绍一种用于个人协议评估的方法,以从专家组中识别出不一致的评分者。我们排除了这些专家,并决定了最佳的专家选择方法,以基于专家的意见提高所构建舌图像数据库的可靠性。邀请了来自中国各地中医诊断领域的50名经验丰富的专家对300种随机选择的舌头图像进行评分。格威特(Gwet)的AC1(一阶一致性系数)用于计算间位和内位者一致性。提出了对个体间的一致性和分歧分数的优化,以评价各个专家的外部一致性。所提出的方法可以成功地优化接口协议。通过比较三种专家的选择方法,使用方法A(包括内部评分者协议> 0.6的专家)将原始协议的原始协议分别从原始协议的0.53 [0.32-0.75]增加到0.64 [0.39-0.80],0.69 [0.63] -0.81]使用方法B(包括分歧评分=“ 0”的专家),以及0.76 [0.67-0.83]使用方法C(包括内部评分者一致性> 0.6&分歧分数=“ 0”的专家)。在这项研究中,我们提供了对单个专家的外部一致性的估计,并且基于专家意见,对每个专家的内部一致性和外部一致性的综合考虑将优于舌图像构建中的任何一个。

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