College of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou 510006, China.
J Healthc Eng. 2021 Oct 29;2021:4699420. doi: 10.1155/2021/4699420. eCollection 2021.
To enhance the depth of excavation and promote the intelligence of acupoint compatibility, a method of constructing weighted network, which combines the attributes of acupoints and supervised learning, is proposed for link prediction. Medical cases of cervical spondylosis with acupuncture treatment are standardized, and a weighted network is constructed according to acupoint attributes. Multiple similarity features are extracted from the network and input into a supervised learning model for prediction. And, the performance of the algorithm is evaluated through evaluation indicators. The experiment finally screened 67 eligible medical cases, and the network model involved 141 acupoint nodes with 1048 edge. Except for the Preferential Attachment similarity index and the Decision Tree model, all other similarity indexes performed well in the model, among which the combination of PI index and Multilayer Perception model had the best prediction effect with an AUC value of 0.9351, confirming the feasibility of weighted networks combined with supervised learning for link prediction, also as a strong support for clinical point selection.
为了提高挖掘深度，促进穴位配伍的智能化，提出了一种结合穴位属性和监督学习的链接预测加权网络构建方法。对颈椎病针刺治疗的医案进行规范，根据穴位属性构建加权网络。从网络中提取多个相似性特征，并输入到监督学习模型中进行预测。然后，通过评价指标来评估算法的性能。实验最终筛选出 67 例合格的医案，网络模型共涉及 141 个穴位节点和 1048 条边。除优先连接相似性指数和决策树模型外，其他相似性指数在模型中表现良好，其中 PI 指数和多层感知机模型的组合预测效果最好，AUC 值为 0.9351，证实了加权网络与监督学习相结合进行链接预测的可行性，也为临床选穴提供了有力支持。
