Recent Advances in the Study of Ancient Books on Traditional Chinese Medicine

The ancient books on traditional Chinese medicine(TCM) are the source of knowledge for TCM physicians. Therapeutic principles and therapeutic methods for healing many diseases are recorded in these ancient TCM books, providing a huge number of references for modern TCM physicians on conducting diagnosis and administering treatment for different diseases. The ancient TCM books can be dated back thousands of years, and this vast knowledge is recorded in different medical books in the form of text. However, it is difficult to systematically assimilate much information in ancient TCM books. At present, many researchers are applying advanced analytical techniques to analyze the text data in the ancient TCM books. Advanced techniques that have been applied include database construction, cognitive linguistic analysis, fuzzy logic, data mining, and artificial intelligence(AI) technology. There are different characteristics in these advanced analytical techniques. In this study, we comprehensively review recent advances in these techniques applied to the study of ancient TCM books. Furthermore, as AI technology is increasingly utilized in the medical field as well as in the study of ancient TCM books, we also review the application of AI technology to the study of ancient TCM books.

The Quest for the Modernization and Internationalization of Traditional Chinese Medicine

Traditional Chinese medicine (TCM) is deeply rooted in ancient Chinese culture and has been practiced by Chinese people for thousands of years in order to maintain their health and fight against disease. This ancient Chinese wisdom has accumulated from the long struggle to cope with various diseases through hundreds or even thousands of trial-and-error practices. However, due to its empirical character, TCM has long been criticized as being deficient in scientific evidence, and is still not widely accepted by the mainstream conventional medical system. The complexity of the chemical components of TCM and the clarification of its mechanisms remain an enormous challenge in the conversion of TCM into an evidence-based medicine. Thanks to incredible progress in biomedical research, TCM has evolved at an astonishing pace in various aspects, as indicated by the 2015 Nobel Prize awarded to Professor Youyou Tu for her discovery of artemisinin.

Ancient and modern medication laws of aromatic Chinese medicines in treating angina pectoris based on data mining

Objective To explore ancient and modern medication laws of aromatic Chinese medicines in treating angina pectoris, and to provide new ideas for the clinical treatment.Methods With “angina pectoris” as the key word, ancient books prescriptions and Chinese patent medicines related to angina pectoris were collected from China National Knowledge Infrastructure(CNKI), Traditional Chinese Medicine Database System, Chinese Medicine Prescription Database, New National Proprietary Chinese Medicine(2 nd edition), and Chinese Pharmacopoeia(2020 edition) from January 1, 2015 to December 31, 2021. Core highfrequency aromatic Chinese medicines were defined, and their potential medication rules were analyzed and summarized. Microsoft Access 2010 was used for data management. Data analysis software, including Excel and IBM SPSS Modeler 18.0 were used for drug association rule analysis, and Cytoscape 3.7.2 for visual display.Results There were 67 ancient books prescriptions and 258 Chinese patent medicines containing aromatic Chinese medicines treating angina pectoris collected from relevant databases. In ancient books prescriptions, there were nine aromatic Chinese medicines with the frequency ≥10, and the most commonly used medicine was Danggui(Angelicae Sinensis Radix), followed by Chenpi(Citri Reticulatae Pericarpium). There were 33 aromatic Chinese medicines with the frequency ≥10 in Chinese patent medicines, and the most commonly used medicine was Danshen(Salviae Miltiorrhizae Radix et Rhizoma), followed by Chuanxiong(Chuanxiong Rhizoma) and Sanqi(Notoginseng Radix et Rhizoma). In ancient books prescriptions, the medicines mainly belonged to intenal-warming medicines, Qi-regulating medicines, and blood circulation promoting and blood stasis removing medicines.There were eight medicine pairs with confidence equal to 100% in ancient books prescriptions, the most frequently used pairs were Chuanxiong(Chuanxiong Rhizoma) +Danggui(Angelicae Sinensis Radix), and Xiangfu(Cyperi Rhizoma) + Chenpi(Citri Reticulatae Pericarpium). In Chinese patent medicines, the aromatic Chinese medicine Chuanxiong(Chuanxiong Rhizoma) could be combined with many other Chinese medicines, among which the Confidence and Support of Chuanxiong(Chuanxiong Rhizoma) + Danshen(Salviae Miltiorrhizae Radix et Rhizoma) were at a high level.Conclusion Aromatic Chinese medicines for the treatment of angina pectoris of coronary heart disease are mainly warm, and the flavors are mainly pungent, sweet, and bitter. They mainly access to the liver, gallbladder, and pericardium meridians. The treatment of angina pectoris of coronary heart disease mainly focuses on warming heart pulse, and promoting blood circulation and removing blood stasis.

Impressions on the Book“On the Standard Nomenclature of Traditional Chinese Medicine”(by Professor XIE Zhu-fan,Foreign Languages Press Beijing, 2003)

Traditional Chinese medicine(TCM)is indubitably on the top of Chinese cultural treasures,deeplyimpressed in the mind of Chinese people.Unfortunately,TCM is facing difficulties in the route towardsinternationalization,up till now,TCM has not yet been granted a legal position for clinical practice in aconsiderable number of countries.So it is our responsibility tO make TCM internationalized and push TCMtobe accepted world—wide,even though we know that it is a long way to go.

A review on different English versions of an ancient classic of Chinese medicine:Huang Di Nei Jing

Huang Di Nei Jing （Nei Jing）, one of the most important classical texts of traditional Chinese medicine, is the foundation upon which today＇s Chinese medicine principles are built. This seminal ancient classic has been translated into English many times by different scholars worldwide. The authors have found thirteen English versions, each of which has its own distinctive features. By reviewing different versions and research achievements of the English translation, the authors try to summarize the translation style, purpose, content and other features of each version, and point out each translation＇s weaknesses, strengths, or significance. The emerging of so many English versions of Nei Jing, an ancient and sophisticated text, shows that scholars have disparate understanding of its language and concepts. Additionally, different educational backgrounds, professions and goals of the translators will all contribute to different perspectives and approaches in the translation. It is understandable that so many versions of translation exist for such an ancient and important classic. However, to achieve truly accurate translation of ancient classical texts of Chinese medicine, all translators should abide by certain basic requirements and translation principles.

Intelligent Prescription-Generating Models of Traditional Chinese Medicine Based on Deep Learning

Objective:This study aimed to construct an intelligent prescription-generating(IPG)model based on deep-learning natural language processing(NLP)technology for multiple prescriptions in Chinese medicine.Materials and Methods:We selected the Treatise on Febrile Diseases and the Synopsis of Golden Chamber as basic datasets with EDA data augmentation,and the Yellow Emperor’s Canon of Internal Medicine,the Classic of the Miraculous Pivot,and the Classic on Medical Problems as supplementary datasets for fine-tuning.We selected the word-embedding model based on the Imperial Collection of Four,the bidirectional encoder representations from transformers(BERT)model based on the Chinese Wikipedia,and the robustly optimized BERT approach(RoBERTa)model based on the Chinese Wikipedia and a general database.In addition,the BERT model was fine-tuned using the supplementary datasets to generate a Traditional Chinese Medicine-BERT model.Multiple IPG models were constructed based on the pretraining strategy and experiments were performed.Metrics of precision,recall,and F1-score were used to assess the model performance.Based on the trained models,we extracted and visualized the semantic features of some typical texts from treatise on febrile diseases and investigated the patterns.Results:Among all the trained models,the RoBERTa-large model performed the best,with a test set precision of 92.22%,recall of 86.71%,and F1-score of 89.38%and 10-fold cross-validation precision of 94.5%±2.5%,recall of 90.47%±4.1%,and F1-score of 92.38%±2.8%.The semantic feature extraction results based on this model showed that the model was intelligently stratified based on different meanings such that the within-layer’s patterns showed the associations of symptom–symptoms,disease–symptoms,and symptom–punctuations,while the between-layer’s patterns showed a progressive or dynamic symptom and disease transformation.Conclusions:Deep-learning-based NLP technology significantly improves the performance of IPG model.In addition,NLP-based semantic feature extraction may be vital to further investigate the ancient Chinese medicine texts.

痛风病中医认识源流考

痛风病是与高尿酸血症、尿酸盐沉积密切相关的慢性、进展性、致残性代谢疾病。本文旨在系统梳理痛风病各阶段相关的中医药古籍记载,厘清其中医药认识发展脉络,明确各时期对痛风病的认知及其特点,为痛风病深入研究和临床防治提供中医药理论支撑。通过查阅先秦至明清时期中医医论、方书、本草及医案106部,从中归纳整理有关痛风病的中医药认识,包括病名、症状、病因病机及治疗用药等,探析其发展源流。先秦时期,中医药古籍零星记载了痛风病各阶段的症状、病名和(或)病因病机;汉至隋唐,医家在扩充病名、症状及病因病机的基础上,提出了多种内外治法,标志着痛风病理论形成;宋元明清,证候分型、鉴别诊断及饮食禁忌等论述丰富了这一理论。中医药对痛风病的认识萌芽于先秦,理论形成于汉至隋唐,丰富发展于宋元明清,逐渐形成了一套较为全面的痛风病中医理论体系。

馆藏优秀传统文化资源再生性保护与价值释放——基于中医药古籍的调查分析

通过调查统计河南省各类图书馆、文博单位等中医药古籍资源存藏、保护、整理和利用情况,分析河南省中医药古籍资源馆藏开发整理存在的问题,提出“加强中医药古籍保护与利用工作的领导与统筹”“深入挖掘整理河南省中医药古籍资源”“确定重点保护对象,推进中医药古籍多层级保护”“加大中医药古籍保护领域复合型人才培养力度”“加强中医药古籍艺术与文化价值的发掘与传播”等推动河南省中医药发展的举措。

传统中药炮制研究的四重证据法

本草文献的传统研究方法主要是以乾嘉学派的考据学为主,是对古籍加以整理、校勘、注疏、辑佚的一种治学方法。近年来,黄璐琦院士将二重证据法运用于本草文献研究中,倡导本草考古,即在本草文献考证研究方法的基础上,进一步以出土的文物为依据,修正和补充历史文献的错漏、局限和缺佚。作者根据炮制文献研究的特殊性,提出在二重证据法的基础上,结合田野调查与炮制古法复原,并称为传统中药炮制研究的四重证据法,其核心内容为古法复原。运用该法着力解决:文献记载的炮制工艺是否真实的问题,古籍中同种药物不同炮制工艺的优劣比较问题,古代炮制文献中的疑难问题,现代中药炮制工艺中存在的问题,为新药研发提供新思路,并列举了具体的运用实例。

基于中医古籍知识库的临床辅助决策系统建设

旨在探索中医古籍知识库在临床辅助决策系统中的应用与建设。中医古籍包含了丰富的临床经验和中医理论知识,然而由于古籍数量庞大且馆藏分散,保存状态参差不齐,使得医生在临床实践难以充分运用。因此,将中医古籍知识库与临床辅助决策系统相结合,逐渐成为提升中医临床诊疗水平的重要趋势之一。本研究收集岭南中医古籍共计278本,涵盖488种疾病,1198种证候,3907种症状,系统对中医古籍进行整理和分类,构建出一个结构化和标准化的中医古籍库,同时关联引入现代临床医学相关知识,形成了综合性的中医临床知识库。本文设计了一套基于规则的推理引擎和机器学习算法,根据患者的症状、体质等信息,从中医古籍知识库中检索出相应的诊断和治疗建议。通过实际应用和测试,验证了基于中医古籍知识库的临床辅助决策系统可提高中医诊疗的有效性,系统的建设不仅有助于传承和发扬中医古籍知识,也为现代中医临床实践提供了有力的支持。

中医正骨学古今教学特点刍议

中医正骨学是一门技术性学科,在古今都占有着重要的医疗地位。文章针对中医正骨学的古代和现代教学特点进行讨论,并根据发展的实际情况分析教学模式的变化特点,以便更好地保持我国中医药治疗的特色和优势,使中医正骨学保持学科特色、发挥学科优势,充分利用现代科学技术,走在科技的前线,与现代高新技术相结合,促进其教学发展。

墨子义利观视域下古代中医义利观分析及其当代价值

古代中医医家行医目的和取酬态度与墨子义利观具有高度相通性。在墨子“贵义尚利”思想的涵养和规约下,古代中医医家形成了以术利人、以义为先的行医目的和义利兼顾、以义获利的取酬态度。以古鉴今,将古代中医义利观的精髓,通过感染教育,培养医务人员以义为上的行医追求;利用课程思政和媒体宣传,鞭策医务人员守住以义制利的行医底线;借助激励机制,构建义利兼顾的薪酬分配等多种路径,有机融摄于当代医德义利观教育,以冀树立符合当今经济和社会发展的医学义利观,为构建和谐医患关系、促进医学健康发展、构建人类卫生健康共同体助力。

从仲景古籍在非医机构的收藏情况谈中医教育

《中华医藏·伤寒金匮类》的编纂需要进行大量的版本调研。调研过程中,作者发现,在仲景古籍的收藏方面,无论是数量还是质量非医学机构都不逊于医学机构。仲景古籍的流传并非仅靠中医界人士,而是涉及整个学术圈。同样,对中医古籍的研究也并非仅限于中医界,跨学科研究蔚然成风,很多非中医人士的中医学术研究成果斐然。这些现象引发多层次的思考,包括对现代社会中医教育的思考。中医教育究竟应该是一个怎样的走向,是只强调学生的中医知识范畴,还是加强学生的综合学识培养,值得深思。

必要的张力--近百年来中西医之辨

中医百年的命运多舛,由表到里都发生了重大变迁。中医学中的古今、中西、体用、新旧之辨是文化大讨论的一部分,其辩论的议题在文化大讨论中都可以找到引子。以上不同方面的辩论是联通的,一定条件下彼此之间可以互换,在进化论的中介和引导下贯穿于中医近代史中。古今、中西、体用、新旧之间的张力让中医学持续受到关注,在批评中螺旋式进步。中医学最终经受住了冲击,找到了平衡,逐步完成了近代向现代的转型。

中国古代医学教育与考试制度概述

我国是世界上最早实行医学教育和考试制度的国家,医学教育起源南朝刘宋时期,考试制度则源于西周,二者基于政治、思想和经济等因素的影响,不断推动中医药事业进步。研究中国古代医学教育与考试制度,总结其中的经验,寻绎其中的规律,是中国医学史研究的一个重要内容,对于我们把握中国医学教育与考试的特色与规律、更好地继承与发展中医学、培养中医学人才,具有重要的现实意义。该文主要概括中国各朝代的医学教育和考试制度,尽力发掘与勾勒我国古代医学教育与考试制度在不同时代的特点、意义及其发展、演变的规律,对于指导中医药院校的教学、培育和选取优秀中医药人才具有重要意义。

中风病古籍病名考辨

中风病古籍挖掘工作当以病名梳理甄别先行。本文对中风病古籍病名进行考辨,认为“中风”初为“中于风”之省文,以“中风”命名中风病始自汉代张仲景,至宋代才逐渐成为中风病的专称;卒中诸证与厥证和中风病的疾病内涵存在交叉,明清时期常被归属到类中风范畴;中风病古籍病证名称命名方式可以概括为以症状、病因、病情、病性命名四种;中风病古籍病名梳理,应秉持以症识病的原则,以中风病主证确定核心范围,以中风病兼证确定疾病外延,能够提高古籍挖掘的精准程度。

基于GPTs的中医知识图谱实体和关系抽取研究

目的研究基于自定义生成型预训练变换模型(GPTs)抽取中医古籍文献中的实体与关系并构建知识图谱的方法,探索其在中医古籍文献整理与挖掘中的潜在优势。方法以《中华医方》部分数据为例,首先通过比较测试国内外常用的GPTs产品处理中医古籍文献数据方面的能力,筛选合适的大语言模型提取《中华医方》中的实体与关系,并将结果以实体表和关系表的形式输出;然后将实体表和关系表导入知识图谱构建工具neo4j,生成中医知识图谱;之后,比较深度学习模型CasRel、GPLinker与GPTs在中医知识图谱构建中的效果,并进行评价。结果实验结果显示,在识别和提取《中华医方》中的实体和关系方面,预训练生成聊天模型4.0(ChatGPT4.0)的GPTs优于其他大语言模型;与传统的深度学习模型比较,其在准确率(P)、召回率(R)、调和平均数(F1)三个指标方面都比其他深度学习模型的表现更好。结论利用GPTs进行中医古籍的实体识别和关系抽取表现出较好的效果,可减少传统方法中人工标注语料和模型训练的需求,有望在中医药领域得到快速推广。然而,此方法也存在一定的局限性,如提示词的编写复杂度高、输出结果可能存在信息省略,以及处理较长文本时上下文窗口长度不足等问题。未来研究需要进一步优化大语言模型的使用策略,以提高其在中医知识图谱构建中的效率和准确性。

AIGC赋能中医古籍活化:Huang-Di大模型的构建

目前中医界已构建大量的古籍资源库,然而数字化研究仍以文献扫描整理、浏览检索等浅层知识服务的实现为主,生成式AI的发展为中医古籍数字化研究提供了新的机遇。文章在Ziya-LLaMA-13B-V1开源模型基础上,通过继续预训练、有监督微调、DPO优化的全流程训练步骤,构建中医古籍生成式对话大语言模型,最后通过自动评估和人工评估验证了其在中医古籍领域的优异性能。自动评估结果表明:训练损失函数成功收敛,各对话类目下BLEU、ROUGE指标值均偏低,侧面反映出该模型具备强大的领域创造力。人工评估结果显示:该模型在古籍知识问答方面性能显著优于现有的中医药垂直领域两类模型,较优于通义千问,部分类目如预防养生的回答能力与ChatGPT(gpt-4)相比略有不足。本研究突破中医古籍数字化固有的研究模式,实现了古籍资源的深度融合与利用,可满足古籍知识解答、中医问诊、养生保健等多元化的知识服务。