Graduate Institute of Clinical Medicine, and Graduate Institute of Traditional Chinese Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Chinese Medicine, Linsen Chinese Medicine Branch, Taipei City Hospital, Taipei 104, Taiwan.
Complement Ther Med. 2013 Dec;21(6):649-59. doi: 10.1016/j.ctim.2013.08.011. Epub 2013 Aug 22.
Pulse palpation was an important part of the traditional Chinese medicine (TCM) vascular examination. It is challenging for new physicians to learn to differentiate between palpations of various pulse types, due to limited comparative learning time with established masters, and so normally it takes many years to master the art. The purpose of this study was to introduce an offline TCM skill evaluation and comparison system that makes available learning of palpation without the master's presence. We record patient's radial artery pulse using an existing pressure-based pulse acquisition system, then annotate it with teachers' evaluation when palpating the same patient, assigned as likelihood of it being each pulse type, e.g. wiry, slippery, hesitant. These training data were separated into per-doctor and per-skill databases for evaluation and comparison purposes, using the following novel procedure: each database was used as training data to a panel of time-series data-mining algorithms, driven by two validation tests, with the created training models evaluated in mean-squared-error. Each validation of the panel and training data yielded an array of error terms, and we chose one to quantitatively evaluate palpation techniques, giving way to compute self consistency and mutual-similarity across different practitioners and techniques. Our experiment of two practitioners and 396 per-processing samples yielded the following: one of the physicians has much higher value of self-consistency for all tested pulse types. Also, the two physicians have high similarity in how they palpate the slipper pulse (P) type, but very dissimilar for hesitant (H) type. This system of skill comparisons may be more broadly applied in places where supervised learning algorithms can detect and use meaningful features in the data; we chose a panel of algorithms previously shown to be effective for many time-series types, but specialized algorithms may be added to improve feature-specific aspect of evaluation.
脉象触诊是中医血管检查的重要组成部分。由于与既定大师相比，新医生可用于比较学习的时间有限，因此很难学习区分各种脉象，通常需要多年时间才能掌握这门艺术。本研究旨在介绍一种离线中医技能评估和比较系统，该系统可在没有大师在场的情况下提供触诊学习。我们使用现有的基于压力的脉搏采集系统记录患者的桡动脉脉搏，然后在对同一患者进行触诊时用老师的评估对其进行注释，将其标记为每种脉象（例如弦、滑、涩）的可能性。这些训练数据分为每位医生和每项技能数据库，以便进行评估和比较，使用以下新颖的程序：每个数据库都用作面板时间序列数据挖掘算法的训练数据，由两个验证测试驱动，使用创建的训练模型评估均方误差。面板和训练数据的每个验证都产生了一系列误差项，我们选择其中一个来定量评估触诊技术，从而计算出不同医生和技术之间的自我一致性和相互相似性。我们对两位医生和 396 个预处理样本进行的实验得出以下结果：其中一位医生对所有测试的脉象类型的自我一致性值都要高得多。此外，两位医生在触诊滑脉（P）类型时具有很高的相似性，但在触诊涩脉（H）类型时却非常不同。这种技能比较系统可能更广泛地应用于可以使用监督学习算法检测和使用数据中有意义特征的地方；我们选择了一组以前被证明对许多时间序列类型有效的算法，但可以添加专门的算法来改善评估的特征特定方面。
