Syndrome classification plays a key role in the clinical diagnosis and treatment with traditional Chinese medicine (TCM), aiming to identify the disease type. Given symptoms of a patient, existing approaches for syndrome classification are generally limited to modeling the interaction between symptoms and syndromes while ignoring the induction of state elements. To alleviate this issue, a state-element-aware hypergraph convolutional network (SEHGCN) is proposed to incorporate state elements into syndrome classification and discover high-order semantic relationships among TCM entities through hypergraph convolutional network (HGCN). Specifically, state elements are initially induced from symptoms by an extraction network, then symptoms and state elements are embedded via convolution on patient hypergraph to obtain the latent representation of patients. Finally, syndromes are classified by a multilayer perceptron (MLP). Extensive experiments on two TCM datasets show that the syndrome classification results with this proposed method are significantly improved over other competing methods.

"阴阳自和"理论是阴阳学说的一项重要内容,对人体健康状态的调整具有重要指导意义.中医健康状态包含未病态、欲病态、已病态、病后态,当人体"阴阳自和"机制稳定发挥时,人体处于未病态,当"阴阳自和"机制发挥失常,则处于欲病态和已病态,在病后态时人体"阴阳自和"机制则处于极不稳定状态.从"阴阳自和"理论出发认识2型糖尿病患者中医健康状态,并在"阴阳自和"理论指导下对2型糖尿病患者健康状态进行调整,提出在未病态时需守"自和"以未病先防,在欲病态时需顺"自和"以防微杜渐,在已病态时需调"自和"以既病防变,在病后态时需助"自和"以防故疾再起,从而降低2型糖尿病发病率.

Introduction: Traditional Chinese Medicine (TCM) diagnosis is a reasoning process through expert knowledge, in which syndrome classification is a key step for prescription recommendation and the treatment of patients. Doctors generally differentiate syndrome types according to patients' symptoms and state elements. This paper proposes a syndrome classification method based on graph convolutional network with residual structure, to exploit the potential relationship between symptoms and state elements. Methods: We constructed a graph convolutional network by combining symptoms and state elements for syndrome classification, called Symptoms-State elements Graph Convolutional Network (SSGCN), embedding the inherent logic of TCM diagnosis and treatment with a prescription graph. This graph architecture wherein contained the relationship between symptoms and state elements, and a multi-layer perceptron (MLP) was trained to classify different syndromes.Results: Experiments were conducted on two self-built datasets according to two classic TCM books, i.e., Theories on Febrile Diseases and Traditional Chinese Medicine Prescription Dictionary. Accuracy, precision, recall and F1 score were adopted to evaluate the syndrome classificaiton results. Our proposed SSGCN method achieved accuracy of 75.59%, 69.63%, precision of 69.10%, 76.33%, recall of 75.63%, 66.67% and F1-score of 71.26%, 65.84% in the above two datasets, respectively. The proposed method for syndrome classification outperformed several popular methods including support vector machine, random forest, extreme gradient boosting and convolutional neural network.Conclusions: By constructing a prescription graph in which symptoms are used as nodes and state elements are taken into account for edges, graph convolution is implemted to capture the relationship of symptoms and state elements. This model improves the performance of syndrome classification and can be further extened for some other related applications in TCM.

The Traditional Chinese Medicine Health Status Identification plays an important role in TCM diagnosis and prescription recommendation. In this paper, we propose a method of Status Identification via Graph Attention Network, named SIGAT, which captures the complex medical correlation in the symptom-syndrome graph. More specifically, we construct a symptom-syndrome graph in that symptoms are taken as nodes and the edges are connected by syndromes. And we realize automatic induction of symptom to state element classification by using the attention mechanism and perceptron classifier. Finally, we conduct experiments by using hamming loss, coverage, 0/1 error, ranking loss, average precision, macro-F1 score, and micro-F1 score as evaluation metrics. The results demonstrate that the SIGAT model outperforms comparison algorithms on Traditional Chinese Medicine Prescription Dictionary dataset. The case study results suggest that the proposed method is a valuable way to identify the state element. The application of the graph attention network classification algorithm in TCM health status identification is of high precision and methodological feasibility.

随着社会对中医诊疗设备智能化、便携化和产业化的要求不断提高,中医诊疗芯片研发逐渐成为现代中医诊疗装备研究中的前沿内容,在加快中医药现代化进程中发挥了关键作用.然而,一款芯片的研发并非易事,其中,数据要素模块是中医诊疗芯片研发的基点,主要包含中医规范化诊疗数据库的构建和中医现代化诊疗仪器设备的辅助采集两大方面内容.指令要素模块是中医诊疗芯片研发的支点,主要包含中医思维模型的构建和智能算法模型的构建两大方面内容.载体要素模块是中医诊疗芯片研发的落点,主要包含芯片封装技术的赋能和新型材料与技术的助力两大方面内容.本文结合中医诊疗原理与芯片制作原理从芯之基础、芯之内核以及芯之载体三个方面阐述中医诊疗芯片的研发路径.

Objective: To establish a state identification algorithm model using convolutional neural network model (CNN). Methods: Based on the fundamental principles of traditional Chinese medicine state identification, the study starts with state characterizations from medical cases and establishes a state characterization database. A CNN-based state element induction model (CNN-SEI) is constructed to connect the state characterizations to state elements and to identify states. Results: The model exhibits significant advantages in accuracy and recall rates, and the results of state identification are highly consistent with clinical diagnosis. Conclusion: This algorithm can improve the accuracy of state identification and provide guidance for clinical syndrome differentiation.

The size and shape of the tongue can reflect different pathological changes of the human body in Traditional Chinese Medicine (TCM). Recently, convolutional neural networks (CNNs) have been widely used for the classification of the color, thickness and teeth marks of the tongue. However, only a few works have been devoted to tongue size and shape classification, which is also key evidence for tongue diagnosis. In this work, we proposed an efficient deep network, TSCWNet, for tongue size and shape classification. The proposed TSC-WNet consists of two subnetworks, i.e. TSC-Net and TSC-UNet. While TSC-Net is a straightforward and effective classification backbone, TSC-UNet is built for tongue segmentation and offers complementary beneficial features to enhance the classification performance of the networks. Our classification backbone requires fewer parameters than classic CNNs like AlexNet, VGG16 and ResNet18, and achieves better classification performance. Employing TSC-Net as the encoder, the TSC-UNet was used to provide the segmentation information for helping better tongue size and shape classification. Two different datasets, i.e. FJTCM/SZU and BioHit, were employed for performance evaluation. The experimental results show that TSC-Net achieves at least 2% higher accuracy and F1 score than the baseline networks. Ablation studies show that the fusion of TSC-Net and TSC-UNet at both input and feature levels can further improve the accuracy and F1 score by about 2%. The code is available at: https://github. com/Yating-Huang/TSC-WNet.

在梳理知识驱动及知识工程、数据驱动及大数据等人工智能技术要素的基础上,根据基于知识驱动与基于数据驱动的中医诊疗系统各自的优势与不足,提出基于知识与数据双驱动的人工智能来构建中医诊疗系统,以适用于中医临床.从中医学学科属性、基于逻辑演绎的中医药理论、基于归纳总结的实践经验角度分析构建双驱动诊疗系统的可行性,同时从中医药领域知识图谱、中医药医案大数据、知识图谱与大数据融合方面构建双驱动诊疗系统的实现路径,以期为中医诊疗系统的发展提供思路.

糖尿病足(DF)有高致残、致死及复发率等特点,是DM常见慢性并发症,严重影响患者生活质量.及时筛查、早期干预,是降低糖尿病足溃疡程度、提高患者生活质量的关键.近年来,随着物联网、人工智能技术发展,构建DF预警系统,实时监控DF高危因素成为DM领域研究热点.本文对DF预警系统研究进展进行综述.

目的 通过对《伤寒论》所载112首经方的近20年现代临床研究文献进行回顾性研究,揭示现代中医临床应用经方治疗病证的规律和用方安全情况.方法 检索CNKI数据库中,自2000-2020年以来,以《伤寒论》经方为主方作为研究对象的现代中医临床研究文献,经文献筛选、病名规范分类后,采用文献计量法及内容分析法对所提取的数据加以统计处理.结果 ①最终共纳入3674篇文献,其中运用经方治疗脾胃系病证文献出现频数最高,为990篇(26.9％),治疗内科杂病文献次之,为893篇(24.3％);②在现代中医临床应用中,热度位居前10位的经方分别为半夏泻心汤、小柴胡汤、炙甘草汤、小青龙汤、柴胡加龙骨牡蛎汤、麻黄杏仁甘草石膏汤、真武汤、四逆散、大柴胡汤和当归四逆汤;③在治疗脾胃系病证中应用频数最高的经方为半夏泻心汤(84.7％),治疗内科杂病则为桂枝加葛根汤(92.0％),治疗肺系病证则为小青龙汤(96.1％),治疗心系病证则为炙甘草汤(95.2％),治疗外、妇、儿科病则为当归四逆汤(35.3％),治疗肝胆系病证则为大柴胡汤(42.6％),治疗肾系病证则为猪苓汤(51.3％);④有185篇(5.0％)文献记录了 38首经方临床应用中的不良反应数据,出现频数位居前10位的经方,分别为小柴胡汤、半夏泻心汤、炙甘草汤、柴胡加龙骨牡蛎汤、小青龙汤、桂枝甘草龙骨牡蛎汤、黄连阿胶汤、五苓散、乌梅丸、麻黄杏仁甘草石膏汤.结论 《伤寒论》所载经方可治疗的疾病种类,覆盖临床常见内、外、妇、儿科疾病,尤其在治疗脾胃系病证和内科杂病方面表现突出;经方在现代临床应用中存在个别不良反应,主要集中在少部分经方应用中,以胃肠道反应、皮肤反应以及头晕、口干等为表现.

Syndrome classification is an important step in Traditional Chinese Medicine (TCM) for diagnosis and treatment. In this paper, we propose a multi-graph attention network (MGAT) based method to simulate TCM doctors to infer the syndromes. Specifically, the complex relationships between symptoms and state elements are aggregated using graph attention networks (GAT) and syndromes are classified by a multilayer perceptron (MLP). To verify the effectiveness of the model, extensive experiments are conducted on the Treatise on Febrile Diseases dataset. The experimental results show that the proposed method outperforms several typical methods in terms of accuracy, precision, recall, and F1-score. The MGAT model has high accuracy in syndrome classification and has practical application value.

Ethnopharmacological relevance: The recommendation of herbal prescriptions is a focus of research in traditional Chinese medicine (TCM). Artificial intelligence (AI) algorithms can generate prescriptions by analysing symptom data. Current models mainly focus on the binary relationships between a group of symptoms and a group of TCM herbs. A smaller number of existing models focus on the ternary relationships between TCM symptoms, syndrome-types and herbs. However, the process of TCM diagnosis (symptom analysis) and treatment (prescription) is, in essence, a "multi-ary" (n-ary) relationship. Present models fall short of considering the n-ary relationships between symptoms, state-elements, syndrome-types and herbs. Therefore, there is room for improvement in TCM herbal prescription recommendation models. Purpose: To portray the n-ary relationship, this study proposes a prescription recommendation model based on a multigraph convolutional network (MGCN). It introduces two essential components of the TCM diagnosis process: state-elements and syndrome-types. Methods: The MGCN consists of two modules: a TCM feature-aggregation module and a herbal medicine prediction module. The TCM feature-aggregation module simulates the n-ary relationships between symptoms and prescriptions by constructing a symptom-'state element'-symptom graph (S-e) and a symptom-'syndrome-type'-symptom graph (T-s). The herbal medicine prediction module inputs state-elements, syndrome-types and symptom data and uses a multilayer perceptron (MLP) to predict a corresponding herbal prescription. To verify the effectiveness of the proposed model, numerous quantitative and qualitative experiments were conducted on the Treatise on Febrile Diseases dataset. Results: In the experiments, the MGCN outperformed three other algorithms used for comparison. In addition, the experimental data shows that, of these three algorithms, the SVM performed best. The MGCN was 4.51%, 6.45% and 5.31% higher in Precision@5, Recall@5 and F1-score@5, respectively, than the SVM. We set the K-value to 5 and conducted two qualitative experiments. In the first case, all five herbs in the label were correctly predicted by the MGCN. In the second case, four of the five herbs were correctly predicted. Conclusions: Compared with existing AI algorithms, the MGCN significantly improved the accuracy of TCM herbal prescription recommendations. In addition, the MGCN provides a more accurate TCM prescription herbal recommendation scheme, giving it great practical application value.

"症状性病名"是解决疾病谱不断变化发展中病名诊断的有效方法.本文阐述了"症状性病名"的概念与内涵,从"脏腑""年龄""气血津液、肢体经络"等三个方面初步阐述了"症状性病名"的分类方法,同时指出"症状性病名"的不足之处在于"可能造成诊断歧化""可能掩饰疾病本质""不符合现代疾病认知".研究认为"症状性病名"具有一定的合理性、科学性和优势性,主要原因是"症状性病名"符合医学发展的需要和整体观念,名实相符,利于临床,也利于把握疾病本质和直观疗效评价,有效弥补单纯西医辨病的不足.建议在临床中合理使用"症状性病名",可以有效解决临床新发疾病病名诊断的难点.

借助现代计算机技术实现中医智能方药推荐是中医现代化研究的趋势,然而目前中医智能方药推荐模型多以直接选择推荐成方为主,罕见智能组方的文献报道.中医临床实际情况复杂多变,难见"教科书式"的病人,成方难以满足全部需求.因而文章提出一个在状态辨识基础上的智能组方模型,即对状态辨识结果(状态要素集合)进行分组结构化-形成单证-根据单证选用药群-合成处方,并对该模型进行扩展、运用规则对模型进行约束用以解决推荐成方难以满足需求的情况,且更加符合传统中医思维,体现中医全面、整体、动态、个性化的特点.

本文从"治未病"的理论来源、文化内涵、与现代化技术的结合点以及"治未病"的应用落点等方面阐述中医"治未病"与现代健康管理的有机结合方案,以期在健康中国战略实施大背景下探索出一条符合中国国情并体现中国特色的健康管理之路.

预判疾病传变对中医临床遣方用药有重要指导价值,然而当前中医临证多以经验为主,对于疾病是否传、如何传尚无明确准则.证素辨证可将复杂的证拆解出病位和病性证素,笔者通过对证素间关系的梳理,认为两两证素之间主要存在“易”“已”两个层次,“嗜”“夹”“致”3种关系,且分别代表了疾病的起因、过程和趋势,故而尝试从证素角度预测证素的发展演变趋势.同时,将阐释证素预测与六经传变、卫气营血传变间关联,并选取内伤和外感各1例医案,解析说明如何通过证素预测实现对疾病传变的预判,以更好指导临床“既病防变”.

名老中医是一批精于中医理论、临床、科研工作并有很深理论造诣和临床经验的人,是中医体系的宝贵财富.文章回顾了名老中医学术经验传承工作的难点,认为主要存在传承模式单一、传承效率低下、传承结果不明等问题,并回顾分析了计算机技术在本研究领域的应用情况,提出智能化是推动名老中医学术经验传承工作的破局关键技术之一.围绕做好数据驱动条件下的智能化传承研究提出4个具体实施方案,即智能化传承的根本发展路径是人机结合、以人为主,前提条件是名老中医诊疗数据结构化,核心关键是辨证算法模型和延伸发展是数据挖掘,基于以上4个步骤最终构建立足中医整体思维、结合现代信息技术手段的数据先行、数据驱动下智能化传承的良性循环发展模式.

基于人工智能的中医辅助诊疗系统研发是中医药发展的趋势和必然.由于缺乏系统的理论支撑,现代人工智能的优势难以发挥.本文以脏腑病机为核心,把超图神经网络引入到中医的辨证论治的过程中,同时构建中医症状、中医病机、中医治法、中医方剂、中药五大知识库,实现辨病机、治法、组方、遣药等环节的全链条智能化,构建基于人工智能的证治体系.

目的:探讨生活习惯对中医心、心神的影响,为精神系统疾病防治提供一定的依据.方法:利用中医健康状态调查获得的人口社会学特征及饮食、运动、睡眠等生活习惯数据,采用二分类Logistic回归分析生活习惯对中医心、心神的影响.结果:按时起居、运动是心、心神的保护因素(按时起居OR值分别为0.274、0.208,运动OR值分别为0.679、0.772,P<0.05);喜食口味偏重多油多盐、喜甜食是心神的危险因素(OR值分别为2.969、1.729,P<0.05);以肉食为主或者素食为主都是心的危险因素(OR值分别为2.268、1.694,P<0.05);饮食不规律、不吃早餐、吃宵夜、抽烟、喝酒、焦虑是心、心神的危险因素(OR值分别为2.434、3.796;2.724、1.826;3.290、3.328;4.133、1.715;4.506、2.061;28.926、8.469,P<0.05).结论:不良生活习惯会促进心、心神疾病的发生,培养良好的生活习惯有利于减少心、心神疾病的发生,有利于精神系统疾病的防治.