整合机器学习与人类使用经验以确定中医个性化药物治疗：以难治性高血压为例
Integrating machine learning and human use experience to identify personalized pharmacotherapy in Traditional Chinese Medicine: a case study on resistant hypertension.
作者信息
Qianzi Che, Dasheng Liu, Xinghua Xiang, Yaxin Tian, Feibiao Xie, Wenyuan X U, Jian Liu, Xuejie Wang, Liying Wang, Weiguo Bai, Xuejie Han, Wei Yang
机构信息
Department of Medical Statistics, Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.
Department of Science and Education, Medical Statistics Teaching and Research Office, Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.
出版信息
J Tradit Chin Med. 2025 Feb;45(1):192-200. doi: 10.19852/j.cnki.jtcm.2025.01.019.
OBJECTIVE
To enhance the understanding of identifying personalized pharmacotherapy options in Traditional Chinese Medicine (TCM), and further support the registration of new TCM drugs.
METHODS
Generalized Boosted Models and XGBoost were employed to construct a classification model to identify the bad prognosis factors in resistant hypertension (RH) patients. Furthermore, we used association analysis to explore the rules of "symptom-syndrome" and "symptom-herb" for the major influencing factors, in order to summarize prescription pattern and applicable patients of TCM.
RESULTS
Patients with major adverse cardiac events mostly have complex symptoms of phlegm, stasis, deficiency and fire intermingled with each other, and finally summarized the human experience of using Chinese herbal medicine to precisely intervene in some symptoms of RH patients on the basis of conventional Western medical treatment.
CONCLUSIONS
Machine learning algorithms can make full use of human use experience and evidence to save clinical trial resources and accelerate the development of TCM varieties.
目的
增强对中医个体化药物治疗方案识别的理解，并进一步支持中药新药注册。
方法
采用广义增强模型和XGBoost构建分类模型，以识别顽固性高血压（RH）患者的不良预后因素。此外，我们使用关联分析来探索主要影响因素的“症状-证候”和“症状-药物”规律，以总结中医的处方模式和适用患者。
结果
发生主要不良心脏事件的患者大多具有痰、瘀、虚、火相互夹杂的复杂症状，并最终总结出在西医常规治疗基础上，使用中药精准干预RH患者某些症状的临床经验。
结论
机器学习算法可以充分利用人类用药经验和证据，节省临床试验资源，加速中药品种的研发。
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