关键词: 腧穴选择, K近邻, 特征提取, 症状规范

Abstract: Acupuncture is an important part of Traditional Chinese Medicine（TCM）. Using emerging technologies to mine hidden regularities from acupuncture treatment information can not only help making?greater contributions to the citizens’ health, but also promote the improvement of modern theoretical system of TCM. Choosing acupoints（namely points） is the key to acupuncture treatment, but the research on acupoint selection for acupuncture treatment with modern technology is still in its infancy. To generate acupoints prescription automatically, an algorithm based on K-nearest neighbor method is proposed in this paper. Through analyzing K records which are most similar to the target patient’s record from a patient record set, the algorithm automatically recommends an acupuncture treatment plan for the patient. According to the characteristics of acupuncture clinical data, normalized symptom names, unigrams and bigrams are adopted as features to calculate the record similarity in this paper. An experiment on a clinical acupuncture data set which contains 6267 records is performed to validate the algorithm, and the experimental result shows that using unigrams and bigrams as features are more suitable for the automatic generation of acupoints prescription and its F-measure can achieve respectively 40.30% and 62.71% after deleting and reserving patients’ subsequent visit data.

Key words: acupoints selection, K-nearest neighbor, feature extraction, symptom normalization