目的:本研究通过非监督学习和多层网络分析袁肇凯教授治疗良性甲状腺结节的用药规律和治疗思路,为甲状腺结节的中医药临床治疗提供新思路。方法:收集2023年6月30日至2024年6月30日袁教授治疗甲状腺结节的192张中医处方,采用描述统计、关联规则、聚类分析等方法筛选高频药物和组方,并结合网络药理学分析药物活性成分及作用机制。通过构建药物–靶点–疾病的网络,探索药物与疾病之间的相互关系。结果:研究发现袁教授常用药物主要包括活血化瘀药、化痰止咳药、清热药,治疗策略以化痰散结、清热解毒、疏肝解郁为主。数据挖掘揭示了两组核心组方,分别侧重祛痰清热和活血化瘀,符合甲状腺结节的“痰瘀互结”病机。网络药理学分析表明,这些中药成分通过调节多个靶点(如TNF-α、IL-6)和信号通路(如NF-κB、MAPK通路),增强了治疗效果。结论:研究为袁教授的治疗策略提供了科学依据,表明中药复方通过多靶点、多通路的机制发挥作用。未来需要进一步的临床验证及分子机制研究,为中医药治疗甲状腺结节提供更多证据。Objective: This study aims to explore the medication patterns and treatment strategies of Professor Yuan Zhaokai in the treatment of benign thyroid nodules using unsupervised learning and multilayer network analysis, thereby providing new perspectives for clinical treatment of thyroid nodules with Traditional Chinese Medicine (TCM). Methods: A total of 192 TCM prescriptions used by Professor Yuan in treating thyroid nodules from June 30, 2023, to June 30, 2024, were collected. High-frequency medications and formulas were identified through descriptive statistics, association rules, and cluster analysis. Additionally, network pharmacology was employed to analyze the active components and mechanisms of action of the medications. By constructing drug-target-disease networks, the interactions between drugs and disease
