中医药导报2025，Vol.31Issue(12)：279-283,5.DOI:10.13862/j.cn43-1446/r.2025.12.044
基于随机森林算法的中药处方升降浮沉药性预测研究
Research on Prediction of Ascending,Descending,Floating and Sinking Properties of Traditional Chinese Medicine Prescriptions Based on Random Forest Algorithm
摘要
Abstract
Objective:To explore the application of the random forest algorithm in predicting the ascend-ing,descending,floating and sinking properties of traditional Chinese medicine(TCM)prescriptions,improve the accuracy of prescription analysis,and provide a scientific basis for TCM prescription review.Methods:Using The Collection of Clinical Medicinal Prescriptions by TCM Master Physician YAN Zhenghua as the data source,a database of medical case and prescriptions was constructed with Microsoft Excel 2019.Treatment methods and names of medicinal pieces were standardized,and the ascending,descending,floating and sinking properties of TCM pieces involved in the prescriptions were determined.The ascending,descending,floating and sinking trends of the prescriptions were labeled based on the treatment methods.Combined with the random forest algorithm,an identification model for ascending,descending,floating and sinking properties was constructed and used fo r prediction based on the standardized prescriptions.Results:A total of 411 TCM prescriptions were included,including 59 ascending-floating prescriptions,182 descending-sinking prescriptions,and 170 prescriptions with both ascending-floating and descending-sinking properties.A total of 255 types of TCM pieces were involved,among which 50 types had ascending-floating tendency,155 types had descending-sinking tendency,and 37 types had dual tendencies.In addition,the tendency attributes of 13 types of TCM pieces were not yet clear because they were not included in the Pharmacopoeia of the People's Republic of China(2020 Edition).When the model was trained with prescription composition,core medicines and dosage as variables,the prediction accuracy of prescription properties was the highest.Conclusion:The random forest model has high accuracy and stability in predicting the ascending,descending,floating and sinking properties of TCM pre-scriptions.It can effectively identify and predict the ascending,descending,floating and sinking properties of prescriptions,and can initially assist pharmacists in the property review of TCM prescriptions.关键词
中药处方/随机森林算法/升降浮沉药性/模型/处方分析Key words
traditional Chinese medicine prescriptions/random forest algorithm/ascending,descending,floating and sinking properties/model/prescription analysis分类
医药卫生引用本文复制引用
郭梦蕊,陈红梅,郦春锦,张峰,孙茜茜,翟华强..基于随机森林算法的中药处方升降浮沉药性预测研究[J].中医药导报,2025,31(12):279-283,5.基金项目
国家自然科学基金项目(8237142307) （8237142307）
