[1]陈雨,周天,庞皓玥,等.基于数据挖掘分析胡凯文教授治疗非小细胞肺癌用药规律[J].西部中医药,2026,39(07):60-64.[doi:10.12174/j.issn.2096-9600.2026.07.14]
　CHEN Yu,ZHOU Tian,PANG Haoyue,et al.Analysis of Professor Hu Kaiwen?s Medication Patterns in the Treatment of Non-Small Cell Lung Cancer Based on Data Mining[J].Western Journal of Traditional Chinese Medicine,2026,39(07):60-64.[doi:10.12174/j.issn.2096-9600.2026.07.14]
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基于数据挖掘分析胡凯文教授治疗非小细胞肺癌用药规律()

《西部中医药》[ISSN:2096-9600/CN:62-1204/R]

卷:
39
期数:
2026年07期
页码:
60-64
栏目:
二次研究
出版日期:
2026-07-12

文章信息/Info

Title:
Analysis of Professor Hu Kaiwen?s Medication Patterns in the Treatment of Non-Small Cell Lung Cancer Based on Data Mining
作者:
陈雨1, 周天2, 庞皓玥3, 方雪妮3, 李媛3, 胡凯文2
1.北京医院/国家老年医学中心/老年疾病国家临床医学研究中心/国家卫生健康委老年医学重点实验室/;中国医学科学院老年医学研究院，北京 100730
2.北京中医药大学东方医院，北京 100078
3.北京中医药大学，北京 100029
Author(s):
CHEN Yu1, ZHOU Tian2, PANG Haoyue3, FANG Xueni3, LI Yuan3, HU Kaiwen2
1.Beijing Hospital / National Center for Gerontology / National Clinical Research Center for Geriatric Disorders / The Key Laboratory of Geriatrics, National Health Commission / Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, China
2.Dongfang Hospital of Beijing University of Chinese Medicine, Beijing 100078, China
3.Beijing University of Chinese Medicine, Beijing 100029, China
关键词:
非小细胞肺癌; 胡凯文; 数据挖掘; 关联规则; 聚类分析; 用药规律
Keywords:
non-small cell lung cancer; data mining; association rules; cluster analysis; medication patterns
分类号:
R285
DOI:
10.12174/j.issn.2096-9600.2026.07.14
文献标志码:
A
摘要:
目的基于数据挖掘方法，系统分析胡凯文教授治疗非小细胞肺癌（non-small cell lung cancer，NSCLC）的中药组方与用药规律，为中医临床治疗提供参考。 方法收集胡凯文教授门诊治疗NSCLC的病历资料，建立Excel数据库。统计药物使用频次、四气五味、归经及功效分布；运用SPSS 23.0对高频药物进行系统聚类分析；采用SPSS Modeler 18.0通过Apriori算法进行关联规则分析。 结果共纳入162份有效病历，涉及中药处方162首，包含药物49味，总使用频次2446次。高频药物前5位依次为黄芪（160次）、熟地黄（158次）、当归（156次）、半夏（86次）、薏苡仁（86次）。药性以温性为主（48.16%），药味以甘味最多（59.40%）。归经主要涉及肺经（48.04%）和脾经（51.35%）。功效分类以补虚药（27.86%）和化痰止咳药（13.03%）占比最高。关联规则分析显示常用药物组合包括黄芪-熟地黄-当归等扶正药对，以及半夏-白芥子-生姜、竹茹-枳实-天花粉等化痰降浊药组。聚类分析将高频药物分为12类，代表性组合有：半夏、白芥子、生姜（化痰散结类）；黄芪、甘草（益气扶正类）；当归、熟地黄（养血滋阴类）；天花粉、竹茹、薏苡仁（清热化痰利湿类）。 结论胡凯文教授治疗非小细胞肺癌以“扶正祛邪、标本兼治”为核心原则，在重用黄芪、熟地黄、当归等补益气血药物以扶助正气的基础上，配合半夏、白芥子、竹茹、枳实等化痰降浊、理气散结之品。其用药规律体现了从“痰浊”论治肺积的学术思想，强调通过祛除痰浊等病理产物以恢复气机通畅，从而提高临床疗效。
Abstract:
ObjectiveTo systematically analyze Professor Hu Kaiwen’ s traditional Chinese medicine prescriptions and medication patterns in the treatment of non-small cell lung cancer (NSCLC) based on data mining, the present study aims to provide a reference for clinical TCM treatment. MethodsClinical medical records of Professor Hu Kaiwen’s outpatient treatment of NSCLC were collected to establish an Excel database. The frequency of drug use, distribution of the four natures, five flavors, meridian tropisms, and therapeutic effects were analyzed. Systematic cluster analysis of high-frequency drugs was performed using SPSS 23.0. Association rule analysis was conducted using the Apriori algorithm in SPSS Modeler 18.0. ResultsA total of 162 valid medical records were included, involving 162 TCM prescriptions, containing 49 herbs, with a total frequency of use of 2,446 times. The top five high-frequency herbs were, in descending order, Huangqi (Astragali Radix, 160 times), Shudihuang (Rehmanniae Radix Praeparata, 158 times), and Danggui (Angelicae Sinensis Radix, 156 times), Banxia (Pinelliae Rhizoma, 86 times), and Yiyiren (Coicis Semen, 86 times). In terms of medicinal properties, warm-natured herbs were predominant (48.16%), and among the five flavors, sweet-tasting herbs were the most frequent (59.40%). Regarding meridian tropism, the herbs primarily involved the Lung meridian (48.04%) and the spleen meridian (51.35%). In terms of efficacy classification, deficiency-nourishing herbs (27.86%) and phlegm-resolving and cough-relieving herbs (13.03%) accounted for the highest proportions. Association rule analysis revealed that commonly used herb combinations included Huangqi-Shudihuang-Danggui as a pair of herbs for reinforcing healthy Qi, as well as combinations for resolving phlegm and descending turbidity, such as Banxia-Baijiezi-fresh ginger, Zhuru(Bambusae Caulis in Taeniam)-Zhishi(Aurantii Fructus Immaturus)-Tianhuafen (Trichosanthis Radix). Cluster analysis classified the high-frequency herbs into 12 clusters, with representative combinations including: Banxia, Baijiezi and fresh ginger (phlegm-resolving and stagnation-dispersing group); Huangqi, Gancao (Qi-reinforcing and healthy Qi-fortifying group), Danggui, Shudihuang (blood-nourishing and Yin-replenishing group), and Tianhuafen, Zhuru, Yiyiren (heat-clearing, phlegm-resolving, and dampness-eliminating group). ConclusionProfessor Hu Kaiwen’s treatment of NSCLC is guided by the core principle of "reinforcing healthy Qi to eliminate pathogenic factors, and addressing both the root cause and clinical manifestations." On the basis of heavily using Qi- and blood-tonifying herbs such as Huangqi, Shudihuang, and Danggui to support healthy Qi, he incorporates herbs that resolve phlegm, descend turbidity, regulate Qi, and disperse stagnation, including Banxia, Baijiezi, Zhuru, and Zhishi. His medication patterns reflect the academic thought of treating lung accumulation (Feiji) from the perspective of "phlegm turbidity," emphasizing the removal of pathological products such as phlegm turbidity to restore the free flow of Qi dynamics, thereby enhancing clinical efficacy.

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备注/Memo

备注/Memo:
国家重点研发计划（2018YFC1705102）；首都卫生发展科研专项（首发2018-1-4201）。 陈雨（1995—），男，博士学位，医师。研究方向：恶性肿瘤的中西医结合防治。
更新日期/Last Update: 2026-07-15