Article(id=1304140197550117445, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304140186485543391, articleNumber=null, orderNo=null, doi=10.7501/j.issn.0253-2670.2026.03.018, pmid=null, cstr=null, oa=null, hot=0, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1762963200000, receivedDateStr=2025-11-13, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1788860852848, onlineDateStr=2026-09-08, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1788860852848, onlineIssueDateStr=2026-09-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1788860852848, creator=13701087609, updateTime=1788860852848, updator=13701087609, issue=Issue{id=1304140186485543391, tenantId=1146029695717560320, journalId=1302319053441957962, year='2026', volume='57', issue='3', pageStart='789', pageEnd='1208', issueExtLink='null', onlineDate='null', pubDate='1770825600000', pubDateStr='2026-02-12', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1788860850211, creator='13701087609', updateTime=1788860942564, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1304140573955351430, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304140186485543391, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1304140573955351431, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304140186485543391, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1001, endPage=1013, ext={EN=ArticleExt(id=1304140197902438983, articleId=1304140197550117445, tenantId=1146029695717560320, journalId=1302319053441957962, language=EN, title=Data-driven precision medication for depression: Analyzing multidimensional association of “regimen-adverse reaction-depressive subtypes” in combination of “Xiaoyao Powders-SSRIs” against depression based on Apriori algorithm and hypergraph, columnId=null, journalTitle=Chinese Traditional and Herbal Drugs, columnName=null, runingTitle=null, highlight=null, articleAbstract=Objective To overcome the clinical limitations of selective serotonin reuptake inhibitors (SSRIs), address the inadequate understanding of individual differences and pathological mechanisms in the antidepressant combination of Xiaoyao Powders with SSRIs, achieve precise combination medication, and promote the transformation of depression treatment toward a “data intelligence-driven” model. Methods A systematic search was conducted in CNKI, VIP, and Wanfang databases using combined subject terms: “Xiaoyaosan + Paroxetine” “Xiaoyaosan + Escitalopram/Citalopram” “Xiaoyaosan + Fluoxetine” “Xiaoyaosan + Sertraline” and “Xiaoyaosan + Fluvoxamine”. The search period spanned from the establishment of each database to May 10, 2025. Literatures were screened based on inclusion and exclusion criteria, and key data were extracted. A database titled “Xiaoyao Powders combined with SSRIs for treating depression” was constructed after verification and cleaning. The Apriori algorithm was applied to mine the association rules between “administration regimens and adverse reactions”, with lift used to quantify the association strength. A hypernetwork of “administration regimens-depression types” was built on the basis of hypergraphs, and the PageRank algorithm was integrated to calculate node weights. A comprehensive efficacy factor was introduced to optimize hyperedge weights, enabling precise matching between depression types and optimal traditional Chinese medicine combination regimens. Results A total of 78 literature was valid and included, with the most study on Xiaoyao Powders with Fluoxetine (33) whereas the least study on Xiaoyao Powders with Fluvoxamine (1). When depression subtypes are not differentiated, the combination of Xiaoyao Powders with Fluoxetine exhibits the highest utilization frequency. Jiawei Xiaoyao Powders was the most used among the applicaitons of Xiaoyao Powders with SSRIs (21). Apriori algorithm analysis showed that the combination of Xiaoyao Powders with Paroxetine was associated with a wide range of adverse reactions but weak single-type associations. Strong associations were observed between the combination with Escitalopram/Citalopram and dry mouth or gastrointestinal reactions, as well as between the combination with Sertraline and diarrhea or nausea. Hypergraph analysis revealed that the “Xiaoyao Powders with Fluoxetine” node had the highest hyperdegree, treating seven depression subtypes; the “depression” node also had the highest hyperdegree, matching five combination regimens; the ranking of hyperedge weights clarified the optimal regimens for different depression types. For example, the “Xiaoyao Powders with Escitalopram/Citalopram” combination was prioritized for senile depression patients, while the “Xiaoyao Powders with Fluoxetine” regimen was preferred for postpartum depression patients. Conclusion The Apriori algorithm can accurately analyze the association characteristics between “combination regimens and adverse reactions”, while hypergraphs can effectively achieve high-order matching between “administration regimens and depression types”. The combination of the two methodologies provides a quantitative basis for the precision medication strategy of depression integrating “disease-symptom-medicine”, and lays a foundation for establishing a new evidence-based, quantitative, and predictable paradigm for the combinations of traditional Chinese and Western medicines., authors=XU Wenlu, SUN Lehua, QIN Xuemei, LIU Xiaojie, authorsList=XU Wenlu, SUN Lehua, QIN Xuemei, LIU Xiaojie, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1304140197818552902, articleId=1304140197550117445, tenantId=1146029695717560320, journalId=1302319053441957962, language=CN, title=数据驱动抑郁症精准用药：基于Apriori算法与超图解析逍遥散类方与选择性5-羟色胺再摄取抑制剂联用抗抑郁的“方案-不良反应-抑郁亚型”三阶关联, columnId=1304140194819629763, journalTitle=中草药, columnName=数据挖掘与循证医学, runingTitle=null, highlight=null, articleAbstract=目的 克服选择性5-羟色胺再摄取抑制剂（selective serotonin reuptake inhibitors，SSRIs）临床用药局限，破解逍遥散类方与SSRIs联用抗抑郁的个体差异及病理机制把握不足问题，实现二者精准联用，并推动抑郁症治疗向“数据智能驱动”转型。方法 以“逍遥散＋帕罗西汀”“逍遥散＋（艾司）西酞普兰”“逍遥散＋氟西汀”“逍遥散＋舍曲林”“逍遥散＋氟伏沙明”组合式主题词为检索词，系统检索中国知网、维普、万方数据库，检索时限为建库至2025年5月10日，按纳入、排除标准筛选文献并提取关键数据，通过核查清洗构建“逍遥散类方联用SSRIs治疗抑郁症”数据库；运用Apriori算法挖掘“给药方案-不良反应”关联规律，以提升度量化关联强度；基于超图构建“给药方案-抑郁症类型”超网络，结合PageRank算法计算节点权重，引入综合疗效因子优化超边权重，实现抑郁类型与最优中医药联用方案的精准匹配。结果 最终纳入78篇有效文献，其中逍遥散类方联用氟西汀文献最多（33篇），联用氟伏沙明最少（1篇）；不区分抑郁类型时，逍遥散类方联用氟西汀使用频次最高，加味逍遥散在逍遥散类方中使用频次居首（21次）。Apriori算法显示，逍遥散类方与帕罗西汀联用不良反应类型广但单类关联弱，与（艾司）西酞普兰联用的口干、胃肠道反应及与舍曲林联用的腹泻恶心关联强度高。超图分析中，“逍遥散类方＋氟西汀”节点超度最高，可治疗7种抑郁亚型；“抑郁症”节点超度最高，适配5种联用方案；超边权重排序明确不同抑郁类型的最优方案，如老年抑郁患者人群优先使用“逍遥散类方＋（艾司）西酞普兰”联用方案、产后抑郁人群优先“逍遥散类方＋氟西汀”方案。结论 Apriori算法可精准解析“联用方案-不良反应”关联特征，超图能有效实现“给药方案-抑郁类型”高阶匹配，二者结合为抑郁症“病-症-药”深度融合的精准用药策略提供量化依据，为中西药联用建立循证、量化、可预测的新范式奠定基础。, authors=许文璐1,2 , 孙乐华1,2 , 秦雪梅1,2 , 刘晓节1,2,3 , authorsList=许文璐, 孙乐华, 秦雪梅, 刘晓节, authorCompany=1 山西大学 中医药现代研究中心, 山西 太原 030006; Health Econ , 2018, 27(3): 525-544.
中草药
|数据挖掘与循证医学
2026
, 57
(3) :
1001
-1013
数据驱动抑郁症精准用药：基于Apriori算法与超图解析逍遥散类方与选择性5-羟色胺再摄取抑制剂联用抗抑郁的“方案-不良反应-抑郁亚型”三阶关联
全屏
[Author(id=1307432285825946538, tenantId=1146029695717560320, journalId=null, articleId=1304140197550117445, orderNo=null, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=null, email=null, emailSecond=null, emailThird=null, correspondingAuthor=null, authorType=null, ext={}, companyList=null)]
作者信息
通讯作者:
刘晓节
作者简介:
许文璐: 许文璐，女，硕士研究生，研究方向为中药神经精神药理。E-mail:13137137590@163.com
Data-driven precision medication for depression: Analyzing multidimensional association of “regimen-adverse reaction-depressive subtypes” in combination of “Xiaoyao Powders-SSRIs” against depression based on Apriori algorithm and hypergraph
Affiliations
doi: 10.7501/j.issn.0253-2670.2026.03.018
文章导航
目的 克服选择性5-羟色胺再摄取抑制剂（selective serotonin reuptake inhibitors，SSRIs）临床用药局限，破解逍遥散类方与SSRIs联用抗抑郁的个体差异及病理机制把握不足问题，实现二者精准联用，并推动抑郁症治疗向“数据智能驱动”转型。方法 以“逍遥散＋帕罗西汀”“逍遥散＋（艾司）西酞普兰”“逍遥散＋氟西汀”“逍遥散＋舍曲林”“逍遥散＋氟伏沙明”组合式主题词为检索词，系统检索中国知网、维普、万方数据库，检索时限为建库至2025年5月10日，按纳入、排除标准筛选文献并提取关键数据，通过核查清洗构建“逍遥散类方联用SSRIs治疗抑郁症”数据库；运用Apriori算法挖掘“给药方案-不良反应”关联规律，以提升度量化关联强度；基于超图构建“给药方案-抑郁症类型”超网络，结合PageRank算法计算节点权重，引入综合疗效因子优化超边权重，实现抑郁类型与最优中医药联用方案的精准匹配。结果 最终纳入78篇有效文献，其中逍遥散类方联用氟西汀文献最多（33篇），联用氟伏沙明最少（1篇）；不区分抑郁类型时，逍遥散类方联用氟西汀使用频次最高，加味逍遥散在逍遥散类方中使用频次居首（21次）。Apriori算法显示，逍遥散类方与帕罗西汀联用不良反应类型广但单类关联弱，与（艾司）西酞普兰联用的口干、胃肠道反应及与舍曲林联用的腹泻恶心关联强度高。超图分析中，“逍遥散类方＋氟西汀”节点超度最高，可治疗7种抑郁亚型；“抑郁症”节点超度最高，适配5种联用方案；超边权重排序明确不同抑郁类型的最优方案，如老年抑郁患者人群优先使用“逍遥散类方＋（艾司）西酞普兰”联用方案、产后抑郁人群优先“逍遥散类方＋氟西汀”方案。结论 Apriori算法可精准解析“联用方案-不良反应”关联特征，超图能有效实现“给药方案-抑郁类型”高阶匹配，二者结合为抑郁症“病-症-药”深度融合的精准用药策略提供量化依据，为中西药联用建立循证、量化、可预测的新范式奠定基础。
抑郁症
/
逍遥散类方
/
选择性5-羟色胺再摄取抑制剂
/
中西药联用
/
Apriori算法
/
超图
/
精准用药
Objective To overcome the clinical limitations of selective serotonin reuptake inhibitors (SSRIs), address the inadequate understanding of individual differences and pathological mechanisms in the antidepressant combination of Xiaoyao Powders with SSRIs, achieve precise combination medication, and promote the transformation of depression treatment toward a “data intelligence-driven” model. Methods A systematic search was conducted in CNKI, VIP, and Wanfang databases using combined subject terms: “Xiaoyaosan + Paroxetine” “Xiaoyaosan + Escitalopram/Citalopram” “Xiaoyaosan + Fluoxetine” “Xiaoyaosan + Sertraline” and “Xiaoyaosan + Fluvoxamine”. The search period spanned from the establishment of each database to May 10, 2025. Literatures were screened based on inclusion and exclusion criteria, and key data were extracted. A database titled “Xiaoyao Powders combined with SSRIs for treating depression” was constructed after verification and cleaning. The Apriori algorithm was applied to mine the association rules between “administration regimens and adverse reactions”, with lift used to quantify the association strength. A hypernetwork of “administration regimens-depression types” was built on the basis of hypergraphs, and the PageRank algorithm was integrated to calculate node weights. A comprehensive efficacy factor was introduced to optimize hyperedge weights, enabling precise matching between depression types and optimal traditional Chinese medicine combination regimens. Results A total of 78 literature was valid and included, with the most study on Xiaoyao Powders with Fluoxetine (33) whereas the least study on Xiaoyao Powders with Fluvoxamine (1). When depression subtypes are not differentiated, the combination of Xiaoyao Powders with Fluoxetine exhibits the highest utilization frequency. Jiawei Xiaoyao Powders was the most used among the applicaitons of Xiaoyao Powders with SSRIs (21). Apriori algorithm analysis showed that the combination of Xiaoyao Powders with Paroxetine was associated with a wide range of adverse reactions but weak single-type associations. Strong associations were observed between the combination with Escitalopram/Citalopram and dry mouth or gastrointestinal reactions, as well as between the combination with Sertraline and diarrhea or nausea. Hypergraph analysis revealed that the “Xiaoyao Powders with Fluoxetine” node had the highest hyperdegree, treating seven depression subtypes; the “depression” node also had the highest hyperdegree, matching five combination regimens; the ranking of hyperedge weights clarified the optimal regimens for different depression types. For example, the “Xiaoyao Powders with Escitalopram/Citalopram” combination was prioritized for senile depression patients, while the “Xiaoyao Powders with Fluoxetine” regimen was preferred for postpartum depression patients. Conclusion The Apriori algorithm can accurately analyze the association characteristics between “combination regimens and adverse reactions”, while hypergraphs can effectively achieve high-order matching between “administration regimens and depression types”. The combination of the two methodologies provides a quantitative basis for the precision medication strategy of depression integrating “disease-symptom-medicine”, and lays a foundation for establishing a new evidence-based, quantitative, and predictable paradigm for the combinations of traditional Chinese and Western medicines.
depression
/
Xiaoyao Powders analogs
/
selective serotonin reuptake inhibitors
/
integrated traditional Chinese and Western medicine
/
Apriori algorithm
/
hypergraph
/
precision medication
许文璐, 孙乐华, 秦雪梅, 刘晓节.
数据驱动抑郁症精准用药：基于Apriori算法与超图解析逍遥散类方与选择性5-羟色胺再摄取抑制剂联用抗抑郁的“方案-不良反应-抑郁亚型”三阶关联.
中草药,
2026
, 57
(3)
: 1001
-1013
.
DOI: 10.7501/j.issn.0253-2670.2026.03.018
XU Wenlu, SUN Lehua, QIN Xuemei, LIU Xiaojie.
Data-driven precision medication for depression: Analyzing multidimensional association of “regimen-adverse reaction-depressive subtypes” in combination of “Xiaoyao Powders-SSRIs” against depression based on Apriori algorithm and hypergraph[J].
Chinese Traditional and Herbal Drugs ,
2026
, 57
(3)
: 1001
-1013
.
DOI: 10.7501/j.issn.0253-2670.2026.03.018
国家自然科学基金资助项目(82274360);山西省中医药管理局资助项目(2024ZYY2A032);山西省中医药创新团队(zyytd2024020);山西省中药功效物质研究与利用重点实验室资助(202105D121009);经方扶阳山西省重点实验室开放课题研究基金资助(CPSY202501)
参考文献
引证文献
吴梅, 阮彬彬, 李天平. 选择性5-羟色胺再摄取抑制剂(SSRIs) 类抗抑郁药的快速评价 [J]. 药物评价研究, 2025, 48(4): 952-963.Health Econ , 2018, 27(3): 525-544.
2026年第57卷第3期
PDF下载
25
6
引用本文
BibTeX
文章信息
doi: 10.7501/j.issn.0253-2670.2026.03.018
接收时间：2025-11-13
首发时间：2026-09-08
https://castjournals.cast.org.cn/joweb/zcy/CN/10.7501/j.issn.0253-2670.2026.03.018
引用本文
BibTeX
2种不同金属材料的力学参数
科 属数 种数 占总种数比例 属 种数 占总种数比例 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
关闭全屏
BibTeX
EndNote
RefWorks
TxT
PDF下载
全屏
25
6
关闭全屏
北京市海淀区学院南路86号
010-62199257（仅加盟咨询）
qkjq@cast.org.cn
