Article(id=1242140568097206613, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242140545322135794, articleNumber=null, orderNo=14, doi=10.3981/j.issn.1000-7857.2023.14.012, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1676649600000, receivedDateStr=2023-02-18, revisedDate=1679500800000, revisedDateStr=2023-03-23, acceptedDate=null, acceptedDateStr=null, onlineDate=1692066602307, onlineDateStr=2023-08-15, pubDate=1690473600000, pubDateStr=2023-07-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1692066602307, onlineIssueDateStr=2023-08-15, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774078989287, creator=sys-migrate, updateTime=1774078989287, updator=sys-migrate, issue=Issue{id=1242140545322135794, tenantId=1146029695717560320, journalId=1146031591421210625, year='2023', volume='41', issue='14', pageStart='1', pageEnd='108', issueExtLink='null', onlineDate='null', pubDate='1690473600000', pubDateStr='2023-07-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774078983857, creator='sys-migrate', updateTime=1774078983857, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=101, endPage=108, ext={EN=ArticleExt(id=1242140573440749948, articleId=1242140568097206613, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Application of graph neural network in mental and nervous system diseases and its enlightenment in the field of Traditional Chinese Medicine, columnId=1242140555799503516, journalTitle=Science & Technology Review, columnName=Exclusive：Real world clinical research of Traditional Chinese Medicine, runingTitle=null, highlight=null, articleAbstract=Graph neural network (GNN) is another major development after convolutional neural network, and it belongs to an emerging deep learning method in the field of artificial intelligence. It mainly preserves the topological information in graph data through end-to-end learning from graph to prediction, and overcomes the defect that traditional deep learning cannot be applied to non-Euclidean data. This paper demonstrates the advantages of GNN for processing complex non-Euclidean data through the application progress of GNN in brain-related mental and nervous system diseases. GNN shows great potential in the medical field, furthermore, the network structure of the syndrome differentiation system of Traditional Chinese Medicine (TCM) is similar to the structure of the brain active area, and it is highly compatible with the schematic unstructured data processed by GNN, so GNN has great application prospect in the field of TCM. This article summarizes the application of GNN in the field of TCM and brain-related mental and nervous system diseases, and analyzes the advantages of TCM diagnosis and treatment models from the theoretical level, in order to explore and build a GNN-based model for fitting with the thinking mode of "syndrome differentiation and treatment" and realizing the objective diagnosis in TCM. It provides new means for solving the problems of complex relationship representation in TCM and mining the individual characteristics of patients. And it also provides a favorable tool for revealing the potential mechanism of TCM from multiple perspectives, and developing and improving the science of TCM., authors=LAI Keyun¹, LAI Changsheng², HE Liyun^3*, WANG Guangjun⁴, CHEN Xiao⁵, authorsList=LAI Keyun, LAI Changsheng, HE Liyun, WANG Guangjun, CHEN Xiao, authorCompany=1. Department of Clinical Medicine of Traditional Chinese and Western Medicine, Shaanxi University of Traditional Chinese Medicine, Xianyang 712046, China
2. Yulin Red Cross Hospital, Yulin 537000, China
3. Institute of Clinical Basic Medicine of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100080, China
4. Institute of Acupuncture and Moxibustion, China Academy of Chinese Medical Sciences, Beijing 100700, China
5. Xi'an Traditional Chinese Medical Encephalopathy Hospital, Xi'an 710032, China, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=GUyoHGi1IbiGIIqSS9CTwg==, pdfFileSize=530593, 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=1242140571075158744, articleId=1242140568097206613, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=图神经网络在精神及神经系统疾病的应用及其对中医药领域的启示, columnId=1242140551189967119, journalTitle=科技导报, columnName=专题：中医真实世界临床研究, runingTitle=null, highlight=null, articleAbstract=图神经网络（GNN）是人工智能领域新兴的一种深度学习方法，通过从图到预测的端对端学习保留了图数据中的拓扑信息，克服了传统深度学习无法应用于非欧数据的缺陷。综述了GNN在大脑相关精神及神经系统疾病方面的应用进展，展示了GNN用于处理复杂的非欧氏复杂数据的优势。GNN已在医疗领域显示出巨大的潜力，又由于中医药辨证体系的网状结构与大脑活动区域的结构相似，与GNN所处理的图式非结构化数据具有高度契合性，因此GNN在中医药领域极具应用前景。总结了GNN在中医药领域及大脑相关精神及神经系统疾病方面的应用，由此及彼地从理论层面分析了中医诊疗模型构建的优势。借助GNN模型，可以探索构建拟合中医“辨证论治”思维模式、实现中医客观化诊断的模型，为解决中医中复杂关系表示、挖掘患者个体化特征等问题提供了新的手段，并为多视角揭示中药的潜在作用机制，发展完善中医药学理论体系提供了有利工具。, authors=赖科云¹，赖昌生²，何丽云^3*，王广军⁴，陈霄⁵, authorsList=赖科云，赖昌生，何丽云，王广军，陈霄, authorCompany=1. 陕西中医药大学中西医临床医学系，咸阳 712046
2. 玉林市红十字会医院，玉林 537000
3. 中国中医科学院中医临床基础医学研究所，北京 100080
4. 中国中医科学院针灸研究所，北京 100700
5. 西安中医脑病医院，西安 710032, correspAuthors=null, authorNote=赖科云，硕士研究生，研究方向为中西医结合脑病，电子信箱：zyylky123@163.com, correspAuthorsNote=何丽云（通信作者），研究员，研究方向为中医临床评价方法学，电子信箱：hely3699@163.com, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=GPpKQ9PHrTxnLgEYOEKCHg==, pdfFileSize=530593, 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=中国中医科学院中医针灸循证临床评价体系应用研究项目（ZZ13-ZD-09）)}, authors=null, keywords=[Keyword(id=1242140570508927697, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1242140568097206613, language=CN, orderNo=1, keyword=图神经网络), Keyword(id=1242140570638951124, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1242140568097206613, language=CN, orderNo=1, keyword=图卷积网络), Keyword(id=1242140570773168853, 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