Article(id=1304735422119170636, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304735403429356361, articleNumber=null, orderNo=null, doi=10.7501/j.issn.0253-2670.2026.14.021, pmid=null, cstr=null, oa=null, hot=0, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1775750400000, receivedDateStr=2026-04-10, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1789002765445, onlineDateStr=2026-09-10, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789002765445, onlineIssueDateStr=2026-09-10, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789002765445, creator=13701087609, updateTime=1789002765445, updator=13701087609, issue=Issue{id=1304735403429356361, tenantId=1146029695717560320, journalId=1302319053441957962, year='2026', volume='57', issue='14', pageStart='5353', pageEnd='5788', issueExtLink='null', onlineDate='null', pubDate='1785168000000', pubDateStr='2026-07-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789002760989, creator='13701087609', updateTime=1789002916821, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1304736057073889492, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304735403429356361, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1304736057073889493, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304735403429356361, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=5601, endPage=5612, ext={EN=ArticleExt(id=1304735422416966222, articleId=1304735422119170636, tenantId=1146029695717560320, journalId=1302319053441957962, language=EN, title=Preliminary study on construction methods of intelligent agent for famous doctors diagnosis and treatment of Alzheimer’s disease, columnId=null, journalTitle=Chinese Traditional and Herbal Drugs, columnName=null, runingTitle=null, highlight=null, articleAbstract=Objective To construct an intelligent agent for the diagnosis and treatment of Alzheimer’s disease (AD) based on knowledge of renowned veteran traditional Chinese medicine (TCM) physicians’ knowledge by adopting artificial intelligence techniques including chain-of-thought reasoning, retrieval-augmented generation (RAG), and prompt engineering, so as to support clinical decision-making with expert TCM experience. Methods Relevant literature and monographs regarding AD treatment by prestigious TCM physicians were retrieved, and a total of 16 renowned veteran TCM physicians, including Tian Jinzhou, Zhou Zhongying, and Shen Baofan, were selected. Their medical records, clinical experience, and academic theories were systematically mined to establish a knowledge base of renowned veteran TCM physicians. To address inconsistencies in syndrome differentiation approaches among different physicians, a syndrome-element classification method was adopted to standardize syndrome differentiation.Based on a large language model and the constructed knowledge base, an intelligent agent for the diagnosis and treatment of AD was developed on the Dify 1.9.2 platform, incorporating both an agent-based individual decision-making scheme and an agent-based group decision-making scheme. Symptom clusters extracted from 108 real-world medical records were used as inputs to the agent, which generated syndrome differentiation results and recommended prescriptions, thereby creating an evaluation dataset. A total of 30 cases were randomly selected for a single-blind expert questionnaire evaluation. Experts evaluated the rationality of syndrome differentiation and prescription recommendations generated by three approaches: the original medical records, the agent-based individual decision-making scheme, and the agent-based group decision-making scheme. Results The agent-based group decision-making scheme achieved the highest estimated marginal mean score (3.93 ± 0.14), outperforming both the agent-based individual decision-making scheme (3.47 ± 0.14) and the original medical records (3.16 ± 0.14). The agent-based individual decision-making scheme also outperformed the original medical records. Conclusion By integrating large language model technology with a syndrome-element classification method, this study incorporated the syndrome differentiation experience of multiple renowned veteran TCM physicians and developed an intelligent agent for the diagnosis and treatment of AD that embodies their collective expertise. The agent-based group decision-making scheme achieved higher scores in expert evaluations, suggesting that this approach can leverage the advantages of collaborative decision-making among multiple renowned physicians. The development of this intelligent agent provides a reusable methodological framework and implementation pathway for applying renowned physicians’ knowledge to support clinical decision-making, and offers a model for developing disease-specific intelligent agents that leverage the advantages of group decision-making, thereby laying the foundation for a subsequent prospective validation platform., authors=ZHANG Lingyan, NI Peiwei, LIU Qin, ZHANG Xidan, LIN Junxiang, LAO Yingrong, LIU Wenchen, authorsList=ZHANG Lingyan, NI Peiwei, LIU Qin, ZHANG Xidan, LIN Junxiang, LAO Yingrong, LIU Wenchen, 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=1304735422354051661, articleId=1304735422119170636, tenantId=1146029695717560320, journalId=1302319053441957962, language=CN, title=名医诊治阿尔茨海默病智能体构建方法初探, columnId=1304140194819629763, journalTitle=中草药, columnName=数据挖掘与循证医学, runingTitle=null, highlight=null, articleAbstract=目的 采用思维链、检索增强生成、提示词等人工智能技术构建基于名医知识的阿尔茨海默病（Alzheimer’s disease，AD）诊治智能体，以应用名医知识辅助临床决策。方法 检索名医治疗AD的文献、书籍，选定田金洲、周仲瑛、沈宝藩等16位名医，对其医案、经验和学术理论进行挖掘，建立名医知识库。通过证素分类解决多位名医辨证方法不统一的问题，以大语言模型和知识库为核心，于Dify 1.9.2智能体开发平台构建名医诊治AD智能体，智能体包含个体决策方案与群体决策方案。将108例真实世界医案的症状群作为输入内容，智能体输出证型判断与建议方药，形成测评集。随机抽取其中30例采用单盲法进行专家问卷测评，专家对原医案、智能体个体决策方案、智能体群体决策方案的辨证和处方结果进行合理性打分。结果 在专家合理性评分中，智能体群体决策方案的估算边际平均得分（3.93±0.14）最高，高于智能体个体决策方案（3.47±0.14）和原医案（3.16±0.14）；智能体个体决策方案得分亦高于原医案。结论 以大模型技术结合证素分类方法，整合多位名医的辨证经验，构建了融合群体名医经验的AD诊治智能体。专家合理性评分显示群体决策方案得分较高，提示该方式可发挥多位名医共同决策优势。该智能体的构建为应用名医知识辅助临床决策提供了可复用的方法及路线，为发挥群体决策优势构建名医诊治专病智能体提供了范例，为后续前瞻性验证平台奠定基础。, authors=张玲艳1 , 倪佩薇1 , 刘琴2 , 张曦丹1 , 林骏翔3 , 老膺荣2 , 刘文琛2 , authorsList=张玲艳, 倪佩薇, 刘琴, 张曦丹, 林骏翔, 老膺荣, 刘文琛, authorCompany=1 广州中医药大学第二临床医学院, 广东 广州 510405;
中草药
|数据挖掘与循证医学
2026
, 57
(14) :
5601
-5612
名医诊治阿尔茨海默病智能体构建方法初探
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张玲艳1 , 倪佩薇1 , 刘琴2 , 张曦丹1 , 林骏翔3 , 老膺荣2 , 刘文琛2
作者信息
1 广州中医药大学第二临床医学院, 广东 广州 510405;
通讯作者:
老膺荣
作者简介:
张玲艳: 张玲艳，硕士研究生，从事中医脑病学研究。E-mail:2537581016@qq.com
Preliminary study on construction methods of intelligent agent for famous doctors diagnosis and treatment of Alzheimer’s disease
ZHANG Lingyan, NI Peiwei, LIU Qin, ZHANG Xidan, LIN Junxiang, LAO Yingrong, LIU Wenchen
Affiliations
doi: 10.7501/j.issn.0253-2670.2026.14.021
文章导航
目的 采用思维链、检索增强生成、提示词等人工智能技术构建基于名医知识的阿尔茨海默病（Alzheimer’s disease，AD）诊治智能体，以应用名医知识辅助临床决策。方法 检索名医治疗AD的文献、书籍，选定田金洲、周仲瑛、沈宝藩等16位名医，对其医案、经验和学术理论进行挖掘，建立名医知识库。通过证素分类解决多位名医辨证方法不统一的问题，以大语言模型和知识库为核心，于Dify 1.9.2智能体开发平台构建名医诊治AD智能体，智能体包含个体决策方案与群体决策方案。将108例真实世界医案的症状群作为输入内容，智能体输出证型判断与建议方药，形成测评集。随机抽取其中30例采用单盲法进行专家问卷测评，专家对原医案、智能体个体决策方案、智能体群体决策方案的辨证和处方结果进行合理性打分。结果 在专家合理性评分中，智能体群体决策方案的估算边际平均得分（3.93±0.14）最高，高于智能体个体决策方案（3.47±0.14）和原医案（3.16±0.14）；智能体个体决策方案得分亦高于原医案。结论 以大模型技术结合证素分类方法，整合多位名医的辨证经验，构建了融合群体名医经验的AD诊治智能体。专家合理性评分显示群体决策方案得分较高，提示该方式可发挥多位名医共同决策优势。该智能体的构建为应用名医知识辅助临床决策提供了可复用的方法及路线，为发挥群体决策优势构建名医诊治专病智能体提供了范例，为后续前瞻性验证平台奠定基础。
阿尔茨海默病
/
名老中医
/
智能体
/
检索增强生成
/
证素辨证
/
群体决策
Objective To construct an intelligent agent for the diagnosis and treatment of Alzheimer’s disease (AD) based on knowledge of renowned veteran traditional Chinese medicine (TCM) physicians’ knowledge by adopting artificial intelligence techniques including chain-of-thought reasoning, retrieval-augmented generation (RAG), and prompt engineering, so as to support clinical decision-making with expert TCM experience. Methods Relevant literature and monographs regarding AD treatment by prestigious TCM physicians were retrieved, and a total of 16 renowned veteran TCM physicians, including Tian Jinzhou, Zhou Zhongying, and Shen Baofan, were selected. Their medical records, clinical experience, and academic theories were systematically mined to establish a knowledge base of renowned veteran TCM physicians. To address inconsistencies in syndrome differentiation approaches among different physicians, a syndrome-element classification method was adopted to standardize syndrome differentiation.Based on a large language model and the constructed knowledge base, an intelligent agent for the diagnosis and treatment of AD was developed on the Dify 1.9.2 platform, incorporating both an agent-based individual decision-making scheme and an agent-based group decision-making scheme. Symptom clusters extracted from 108 real-world medical records were used as inputs to the agent, which generated syndrome differentiation results and recommended prescriptions, thereby creating an evaluation dataset. A total of 30 cases were randomly selected for a single-blind expert questionnaire evaluation. Experts evaluated the rationality of syndrome differentiation and prescription recommendations generated by three approaches: the original medical records, the agent-based individual decision-making scheme, and the agent-based group decision-making scheme. Results The agent-based group decision-making scheme achieved the highest estimated marginal mean score (3.93 ± 0.14), outperforming both the agent-based individual decision-making scheme (3.47 ± 0.14) and the original medical records (3.16 ± 0.14). The agent-based individual decision-making scheme also outperformed the original medical records. Conclusion By integrating large language model technology with a syndrome-element classification method, this study incorporated the syndrome differentiation experience of multiple renowned veteran TCM physicians and developed an intelligent agent for the diagnosis and treatment of AD that embodies their collective expertise. The agent-based group decision-making scheme achieved higher scores in expert evaluations, suggesting that this approach can leverage the advantages of collaborative decision-making among multiple renowned physicians. The development of this intelligent agent provides a reusable methodological framework and implementation pathway for applying renowned physicians’ knowledge to support clinical decision-making, and offers a model for developing disease-specific intelligent agents that leverage the advantages of group decision-making, thereby laying the foundation for a subsequent prospective validation platform.
Alzheimer’s disease
/
renowned veteran traditional Chinese medicine physicians
/
intelligent agent
/
retrieval-augmented generation
/
syndrome-element differentiation
/
group decision-making
张玲艳, 倪佩薇, 刘琴, 张曦丹, 林骏翔, 老膺荣, 刘文琛.
名医诊治阿尔茨海默病智能体构建方法初探.
中草药,
2026
, 57
(14)
: 5601
-5612
.
DOI: 10.7501/j.issn.0253-2670.2026.14.021
ZHANG Lingyan, NI Peiwei, LIU Qin, ZHANG Xidan, LIN Junxiang, LAO Yingrong, LIU Wenchen.
Preliminary study on construction methods of intelligent agent for famous doctors diagnosis and treatment of Alzheimer’s disease[J].
Chinese Traditional and Herbal Drugs ,
2026
, 57
(14)
: 5601
-5612
.
DOI: 10.7501/j.issn.0253-2670.2026.14.021
参考文献
引证文献
Scheltens P, De Strooper B, Kivipelto M, et al. Alzheimer’s disease [J]. Lancet, 2021, 397(10284): 1577-1590.
2026年第57卷第14期
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doi: 10.7501/j.issn.0253-2670.2026.14.021
接收时间：2026-04-10
首发时间：2026-09-10
https://castjournals.cast.org.cn/joweb/zcy/CN/10.7501/j.issn.0253-2670.2026.14.021
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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
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