Department of Science in Korean Medicine, Graduate School, Kyung Hee University, Seoul, 130-701, Republic of Korea.
Acupuncture & Meridian Science Research Center, College of Korean Medicine, Kyung Hee University, Seoul, 130-701, Republic of Korea.
Front Med. 2019 Feb;13(1):112-120. doi: 10.1007/s11684-017-0582-z. Epub 2018 Apr 12.
Comprehension of the medical diagnoses of doctors and treatment of diseases is important to understand the underlying principle in selecting appropriate acupoints. The pattern recognition process that pertains to symptoms and diseases and informs acupuncture treatment in a clinical setting was explored. A total of 232 clinical records were collected using a Charting Language program. The relationship between symptom information and selected acupoints was trained using an artificial neural network (ANN). A total of 11 hidden nodes with the highest average precision score were selected through a tenfold cross-validation. Our ANN model could predict the selected acupoints based on symptom and disease information with an average precision score of 0.865 (precision, 0.911; recall, 0.811). This model is a useful tool for diagnostic classification or pattern recognition and for the prediction and modeling of acupuncture treatment based on clinical data obtained in a real-world setting. The relationship between symptoms and selected acupoints could be systematically characterized through knowledge discovery processes, such as pattern identification.
理解医生的医学诊断和疾病治疗对于理解选择合适穴位的基本原则很重要。本研究旨在探讨与症状和疾病相关的模式识别过程，并为临床针灸治疗提供信息。使用图表语言程序共收集了 232 份临床记录。使用人工神经网络（ANN）对症状信息和所选穴位之间的关系进行训练。通过十折交叉验证，选择了具有最高平均精度评分的 11 个隐藏节点。我们的 ANN 模型可以根据症状和疾病信息预测所选穴位，平均精度评分为 0.865（精度为 0.911；召回率为 0.811）。该模型是基于真实世界环境中获得的临床数据进行诊断分类或模式识别以及预测和模拟针灸治疗的有用工具。通过知识发现过程（如模式识别）可以系统地描述症状与所选穴位之间的关系。
