1
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Chen Z, Zhang D, Liu C, Wang H, Jin X, Yang F, Zhang J. Traditional Chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning. Integr Med Res 2024; 13:101019. [PMID: 38298865 PMCID: PMC10826311 DOI: 10.1016/j.imr.2023.101019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 12/02/2023] [Accepted: 12/17/2023] [Indexed: 02/02/2024] Open
Abstract
Background With the development of traditional Chinese medicine (TCM) syndrome knowledge accumulation and artificial intelligence (AI), this study proposes a holistic TCM syndrome differentiation model for the classification prediction of multiple TCM syndromes based on deep learning and accelerates the construction of modern foundational TCM equipment. Methods We searched publicly available TCM guidelines and textbooks for expert knowledge and validated these sources using ten-fold cross-validation. Based on the BERT and CNN models, with the classification constraints from TCM holistic syndrome differentiation, the TCM-BERT-CNN model was constructed, which completes the end-to-end TCM holistic syndrome text classification task through symptom input and syndrome output. We assessed the performance of the model using precision, recall, and F1 scores as evaluation metrics. Results The TCM-BERT-CNN model had a higher precision (0.926), recall (0.9238), and F1 score (0.9247) than the BERT, TextCNN, LSTM RNN, and LSTM ATTENTION models and achieved superior results in model performance and predictive classification of most TCM syndromes. Symptom feature visualization demonstrated that the TCM-BERT-CNN model can effectively identify the correlation and characteristics of symptoms in different syndromes with a strong correlation, which conforms to the diagnostic characteristics of TCM syndromes. Conclusions The TCM-BERT-CNN model proposed in this study is in accordance with the TCM diagnostic characteristics of holistic syndrome differentiation and can effectively complete diagnostic prediction tasks for various TCM syndromes. The results of this study provide new insights into the development of deep learning models for holistic syndrome differentiation in TCM.
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Affiliation(s)
- Zhe Chen
- Evidence-based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- National Key Laboratory of Chinese Medicine Modernization, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Dong Zhang
- Evidence-based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Chunxiang Liu
- Evidence-based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Hui Wang
- Evidence-based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xinyao Jin
- Evidence-based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Fengwen Yang
- Evidence-based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Junhua Zhang
- Evidence-based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
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2
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Tian D, Chen W, Xu D, Xu L, Xu G, Guo Y, Yao Y. A review of traditional Chinese medicine diagnosis using machine learning: Inspection, auscultation-olfaction, inquiry, and palpation. Comput Biol Med 2024; 170:108074. [PMID: 38330826 DOI: 10.1016/j.compbiomed.2024.108074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 12/15/2023] [Accepted: 01/27/2024] [Indexed: 02/10/2024]
Abstract
Traditional Chinese medicine (TCM) is an essential part of the Chinese medical system and is recognized by the World Health Organization as an important alternative medicine. As an important part of TCM, TCM diagnosis is a method to understand a patient's illness, analyze its state, and identify syndromes. In the long-term clinical diagnosis practice of TCM, four fundamental and effective diagnostic methods of inspection, auscultation-olfaction, inquiry, and palpation (IAOIP) have been formed. However, the diagnostic information in TCM is diverse, and the diagnostic process depends on doctors' experience, which is subject to a high-level subjectivity. At present, the research on the automated diagnosis of TCM based on machine learning is booming. Machine learning, which includes deep learning, is an essential part of artificial intelligence (AI), which provides new ideas for the objective and AI-related research of TCM. This paper aims to review and summarize the current research status of machine learning in TCM diagnosis. First, we review some key factors for the application of machine learning in TCM diagnosis, including data, data preprocessing, machine learning models, and evaluation metrics. Second, we review and summarize the research and applications of machine learning methods in TCM IAOIP and the synthesis of the four diagnostic methods. Finally, we discuss the challenges and research directions of using machine learning methods for TCM diagnosis.
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Affiliation(s)
- Dingcheng Tian
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110819, China
| | - Weihao Chen
- Research Institute for Medical and Biological Engineering, Ningbo University, Ningbo, 315211, China
| | - Dechao Xu
- Research Institute for Medical and Biological Engineering, Ningbo University, Ningbo, 315211, China
| | - Lisheng Xu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110819, China
| | - Gang Xu
- The First Affiliated Hospital of Liaoning University of TraditionalChinese Medicine, Shenyang, 110000, China
| | - Yaochen Guo
- The Affiliated Hospital of the Medical School of Ningbo University, Ningbo, 315020, China
| | - Yudong Yao
- Research Institute for Medical and Biological Engineering, Ningbo University, Ningbo, 315211, China.
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3
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Li W, Ge X, Liu S, Xu L, Zhai X, Yu L. Opportunities and challenges of traditional Chinese medicine doctors in the era of artificial intelligence. Front Med (Lausanne) 2024; 10:1336175. [PMID: 38274445 PMCID: PMC10808796 DOI: 10.3389/fmed.2023.1336175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 12/27/2023] [Indexed: 01/27/2024] Open
Abstract
With the exponential advancement of artificial intelligence (AI) technology, the realm of medicine is experiencing a paradigm shift, engendering a multitude of prospects and trials for healthcare practitioners, encompassing those devoted to the practice of traditional Chinese medicine (TCM). This study explores the evolving landscape for TCM practitioners in the AI era, emphasizing that while AI can be helpful, it cannot replace the role of TCM practitioners. It is paramount to underscore the intrinsic worth of human expertise, accentuating that artificial intelligence (AI) is merely an instrument. On the one hand, AI-enabled tools like intelligent symptom checkers, diagnostic assistance systems, and personalized treatment plans can augment TCM practitioners' expertise and capacity, improving diagnosis accuracy and treatment efficacy. AI-empowered collaborations between Western medicine and TCM can strengthen holistic care. On the other hand, AI may disrupt conventional TCM workflow and doctor-patient relationships. Maintaining the humanistic spirit of TCM while embracing AI requires upholding professional ethics and establishing appropriate regulations. To leverage AI while retaining the essence of TCM, practitioners need to hone holistic analytical skills and see AI as complementary. By highlighting promising applications and potential risks of AI in TCM, this study provides strategic insights for stakeholders to promote the integrated development of AI and TCM for better patient outcomes. With proper implementation, AI can become a valuable assistant for TCM practitioners to elevate healthcare quality.
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Affiliation(s)
- Wenyu Li
- School of Marxism, Capital Normal University, Beijing, China
| | - Xiaolei Ge
- Wangjing Hospital of China Academy of Traditional Chinese Medicine, Beijing, China
| | - Shuai Liu
- Graduate School of Chinese Academy of Traditional Chinese Medicine, Beijing, China
| | - Lili Xu
- Graduate School of Chinese Academy of Traditional Chinese Medicine, Beijing, China
| | - Xu Zhai
- Wangjing Hospital of China Academy of Traditional Chinese Medicine, Beijing, China
| | - Linyong Yu
- China Academy of Chinese Medical Sciences, Beijing, China
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4
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Li Y, Zhou K, Meng Y. Application of prescription analysis in pharmaceutical care of Chinese pharmacy. Minerva Surg 2023; 78:739-741. [PMID: 35762931 DOI: 10.23736/s2724-5691.22.09660-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Affiliation(s)
- Yuan Li
- Heilongjiang University of Chinese Medicine, Harbin, China
| | - Kun Zhou
- Department of Clinical Laboratory, Beidahuang Industry Group General Hospital, Harbin, China
| | - Ying Meng
- Personnel Section, Beidahuang Industry Group General Hospital, Harbin, China -
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5
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Fan M, Jin C, Li D, Deng Y, Yao L, Chen Y, Ma YL, Wang T. Multi-level advances in databases related to systems pharmacology in traditional Chinese medicine: a 60-year review. Front Pharmacol 2023; 14:1289901. [PMID: 38035021 PMCID: PMC10682728 DOI: 10.3389/fphar.2023.1289901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 11/03/2023] [Indexed: 12/02/2023] Open
Abstract
The therapeutic effects of traditional Chinese medicine (TCM) involve intricate interactions among multiple components and targets. Currently, computational approaches play a pivotal role in simulating various pharmacological processes of TCM. The application of network analysis in TCM research has provided an effective means to explain the pharmacological mechanisms underlying the actions of herbs or formulas through the lens of biological network analysis. Along with the advances of network analysis, computational science has coalesced around the core chain of TCM research: formula-herb-component-target-phenotype-ZHENG, facilitating the accumulation and organization of the extensive TCM-related data and the establishment of relevant databases. Nonetheless, recent years have witnessed a tendency toward homogeneity in the development and application of these databases. Advancements in computational technologies, including deep learning and foundation model, have propelled the exploration and modeling of intricate systems into a new phase, potentially heralding a new era. This review aims to delves into the progress made in databases related to six key entities: formula, herb, component, target, phenotype, and ZHENG. Systematically discussions on the commonalities and disparities among various database types were presented. In addition, the review raised the issue of research bottleneck in TCM computational pharmacology and envisions the forthcoming directions of computational research within the realm of TCM.
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Affiliation(s)
- Mengyue Fan
- Innovation Research Institute of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Ching Jin
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, United States
| | - Daping Li
- Innovation Research Institute of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yingshan Deng
- College of Acupuncture and Massage, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Lin Yao
- Innovation Research Institute of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yongjun Chen
- Innovation Research Institute of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yu-Ling Ma
- Oxford Chinese Medicine Research Centre, University of Oxford, Oxford, United Kingdom
| | - Taiyi Wang
- Innovation Research Institute of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
- Oxford Chinese Medicine Research Centre, University of Oxford, Oxford, United Kingdom
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6
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Wang X, Xie Y, Yang X, Gu D. Internet-Based Healthcare Knowledge Service for Improvement of Chinese Medicine Healthcare Service Quality. Healthcare (Basel) 2023; 11:2170. [PMID: 37570410 PMCID: PMC10418357 DOI: 10.3390/healthcare11152170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 07/21/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
With the development of new-generation information technology and increasing health needs, the requirements for Chinese medicine (CM) services have shifted toward the 5P medical mode, which emphasizes preventive, predictive, personalized, participatory, and precision medicine. This implies that CM knowledge services need to be smarter and more sophisticated. This study adopted a bibliometric approach to investigate the current state of development of CM knowledge services, and points out that accurate knowledge service is an inevitable requirement for the modernization of CM. We summarized the concept of smart CM knowledge services and highlighted its main features, including medical homogeneity, knowledge service intelligence, integration of education and research, and precision medicine. Additionally, we explored the intelligent service method of traditional Chinese medicine under the 5P medical mode to support CM automatic knowledge organization and safe sharing, human-machine collaborative knowledge discovery and personalized dynamic knowledge recommendation. Finally, we summarized the innovative modes of CM knowledge services. Our research will guide the quality assurance and innovative development of the traditional Chinese medicine knowledge service model in the era of digital intelligence.
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Affiliation(s)
- Xiaoyu Wang
- The Department of Pharmacy, Anhui University of Traditional Chinese Medicine, Hefei 230031, China;
| | - Yi Xie
- The School of Management, Hefei University of Technology, Hefei 230009, China; (X.Y.); (D.G.)
| | - Xuejie Yang
- The School of Management, Hefei University of Technology, Hefei 230009, China; (X.Y.); (D.G.)
| | - Dongxiao Gu
- The School of Management, Hefei University of Technology, Hefei 230009, China; (X.Y.); (D.G.)
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7
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Wang ZY, Guo ZH. Intelligent Chinese Medicine: A New Direction Approach for Integrative Medicine in Diagnosis and Treatment of Cardiovascular Diseases. Chin J Integr Med 2023:10.1007/s11655-023-3639-7. [PMID: 37222830 DOI: 10.1007/s11655-023-3639-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/07/2023] [Indexed: 05/25/2023]
Abstract
High mortality rates from cardiovascular diseases (CVDs) persist worldwide. Older people are at a higher risk of developing these diseases. Given the current high treatment cost for CVDs, there is a need to prevent CVDs and or develop treatment alternatives. Western and Chinese medicines have been used to treat CVDs. However, several factors, such as inaccurate diagnoses, non-standard prescriptions, and poor adherence behavior, lower the benefits of the treatments by Chinese medicine (CM). Artificial intelligence (AI) is increasingly used in clinical diagnosis and treatment, especially in assessing efficacy of CM in clinical decision support systems, health management, new drug research and development, and drug efficacy evaluation. In this study, we explored the role of AI in CM in the diagnosis and treatment of CVDs, and discussed application of AI in assessing the effect of CM on CVDs.
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Affiliation(s)
- Zi-Yan Wang
- The First Clinical College of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410208, China
| | - Zhi-Hua Guo
- School of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410208, China.
- Hunan Key Laboratory of Colleges and Universities of Intelligent Traditional Chinese Medicine Diagnosis and Preventive Treatment of Chronic Diseases of Hunan Universities of Chinese Medicine, Changsha, 410208, China.
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8
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Ma S, Liu J, Li W, Liu Y, Hui X, Qu P, Jiang Z, Li J, Wang J. Machine learning in TCM with natural products and molecules: current status and future perspectives. Chin Med 2023; 18:43. [PMID: 37076902 PMCID: PMC10116715 DOI: 10.1186/s13020-023-00741-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 03/28/2023] [Indexed: 04/21/2023] Open
Abstract
Traditional Chinese medicine (TCM) has been practiced for thousands of years with clinical efficacy. Natural products and their effective agents such as artemisinin and paclitaxel have saved millions of lives worldwide. Artificial intelligence is being increasingly deployed in TCM. By summarizing the principles and processes of deep learning and traditional machine learning algorithms, analyzing the application of machine learning in TCM, reviewing the results of previous studies, this study proposed a promising future perspective based on the combination of machine learning, TCM theory, chemical compositions of natural products, and computational simulations based on molecules and chemical compositions. In the first place, machine learning will be utilized in the effective chemical components of natural products to target the pathological molecules of the disease which could achieve the purpose of screening the natural products on the basis of the pathological mechanisms they target. In this approach, computational simulations will be used for processing the data for effective chemical components, generating datasets for analyzing features. In the next step, machine learning will be used to analyze the datasets on the basis of TCM theories such as the superposition of syndrome elements. Finally, interdisciplinary natural product-syndrome research will be established by unifying the results of the two steps outlined above, potentially realizing an intelligent artificial intelligence diagnosis and treatment model based on the effective chemical components of natural products under the guidance of TCM theory. This perspective outlines an innovative application of machine learning in the clinical practice of TCM based on the investigation of chemical molecules under the guidance of TCM theory.
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Affiliation(s)
- Suya Ma
- Guang'anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China
| | - Jinlei Liu
- Guang'anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China
| | - Wenhua Li
- Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Yongmei Liu
- Guang'anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China
| | - Xiaoshan Hui
- Guang'anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China
| | - Peirong Qu
- Guang'anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China
| | - Zhilin Jiang
- Guang'anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China
| | - Jun Li
- Guang'anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China.
| | - Jie Wang
- Guang'anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China.
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9
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Liu X, Huang X, Zhao J, Su Y, Shen L, Duan Y, Gong J, Zhang Z, Piao S, Zhu Q, Rong X, Guo J. Application of machine learning in Chinese medicine differentiation of dampness-heat pattern in patients with type 2 diabetes mellitus. Heliyon 2023; 9:e13289. [PMID: 36873141 PMCID: PMC9975099 DOI: 10.1016/j.heliyon.2023.e13289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/26/2023] [Accepted: 01/27/2023] [Indexed: 02/15/2023] Open
Abstract
Background China has become the country with the largest number of people with type 2 diabetes mellitus (T2DM), and Chinese medicine (CM) has unique advantages in preventing and treating T2DM, while accurate pattern differentiation is the guarantee for proper treatment. Objective The establishment of the CM pattern differentiation model of T2DM is helpful to the pattern diagnosis of the disease. At present, there are few studies on dampness-heat pattern differentiation models of T2DM. Therefore, we establish a machine learning model, hoping to provide an efficient tool for the pattern diagnosis of CM for T2DM in the future. Methods A total of 1021 effective samples of T2DM patients from ten CM hospitals or clinics were collected by a questionnaire including patients' demographic and dampness-heat-related symptoms and signs. All information and the diagnosis of the dampness-heat pattern of patients were completed by experienced CM physicians at each visit. We applied six machine learning algorithms (Artificial Neural Network [ANN], K-Nearest Neighbor [KNN], Naïve Bayes [NB], Support Vector Machine [SVM], Extreme Gradient Boosting [XGBoost] and Random Forest [RF]) and compared their performance. And then we also utilized Shapley additive explanation (SHAP) method to explain the best performance model. Results The XGBoost model had the highest AUC (0.951, 95% CI 0.925-0.978) among the six models, with the best sensitivity, accuracy, F1 score, negative predictive value, and excellent specificity, precision, and positive predictive value. The SHAP method based on XGBoost showed that slimy yellow tongue fur was the most important sign in dampness-heat pattern diagnosis. The slippery pulse or rapid-slippery pulse, sticky stool with ungratifying defecation also performed an important role in this diagnostic model. Furthermore, the red tongue acted as an important tongue sign for the dampness-heat pattern. Conclusion This study constructed a dampness-heat pattern differentiation model of T2DM based on machine learning. The XGBoost model is a tool with the potential to help CM practitioners make quick diagnosis decisions and contribute to the standardization and international application of CM patterns.
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Affiliation(s)
- Xinyu Liu
- Guangdong Metabolic Diseases Research Center of Integrated Chinese and Western Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Key Laboratory of Glucolipid Metabolic Disorder, Ministry of Education of China, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Guangdong TCM Key Laboratory for Metabolic Diseases, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Institute of Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Xiaoqiang Huang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, 510006, China
| | - Jindong Zhao
- The First Affiliated Hospital of Anhui University of Chinese, Hefei, 230031, China
| | - Yanjin Su
- Shaanxi University of Chinese Medicine, Xi'an, 712046, China
| | - Lu Shen
- Shaanxi Provincial Hospital of Traditional Chinese Medicine, Xi'an, 710003, China
| | - Yuhong Duan
- Affiliated Hospital of Shannxi University of Chinese Medicine, Xi'an, 712000, China
| | - Jing Gong
- Department of Integrated Traditional Chinese and Western Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Zhihai Zhang
- The First Affiliated Hospital of Xiamen University, Xiamen, 361003, China
| | - Shenghua Piao
- Guangdong Metabolic Diseases Research Center of Integrated Chinese and Western Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Key Laboratory of Glucolipid Metabolic Disorder, Ministry of Education of China, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Guangdong TCM Key Laboratory for Metabolic Diseases, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Institute of Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Qing Zhu
- Guangdong Metabolic Diseases Research Center of Integrated Chinese and Western Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Key Laboratory of Glucolipid Metabolic Disorder, Ministry of Education of China, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Guangdong TCM Key Laboratory for Metabolic Diseases, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Institute of Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Xianglu Rong
- Guangdong Metabolic Diseases Research Center of Integrated Chinese and Western Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Key Laboratory of Glucolipid Metabolic Disorder, Ministry of Education of China, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Guangdong TCM Key Laboratory for Metabolic Diseases, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Institute of Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Jiao Guo
- Guangdong Metabolic Diseases Research Center of Integrated Chinese and Western Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Key Laboratory of Glucolipid Metabolic Disorder, Ministry of Education of China, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Guangdong TCM Key Laboratory for Metabolic Diseases, Guangdong Pharmaceutical University, Guangzhou, 510006, China.,Institute of Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China
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10
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Knowledge-Based Recurrent Neural Network for TCM Cerebral Palsy Diagnosis. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:7708376. [PMID: 36276852 PMCID: PMC9581687 DOI: 10.1155/2022/7708376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 08/30/2022] [Indexed: 11/07/2022]
Abstract
Cerebral palsy is one of the most prevalent neurological disorders and the most frequent cause of disability. Identifying the syndrome by patients' symptoms is the key to traditional Chinese medicine (TCM) cerebral palsy treatment. Artificial intelligence (AI) is advancing quickly in several sectors, including TCM. AI will considerably enhance the dependability and precision of diagnoses, expanding effective treatment methods' usage. Thus, for cerebral palsy, it is necessary to build a decision-making model to aid in the syndrome diagnosis process. While the recurrent neural network (RNN) model has the potential to capture the correlation between symptoms and syndromes from electronic medical records (EMRs), it lacks TCM knowledge. To make the model benefit from both TCM knowledge and EMRs, unlike the ordinary training routine, we begin by constructing a knowledge-based RNN (KBRNN) based on the cerebral palsy knowledge graph for domain knowledge. More specifically, we design an evolution algorithm for extracting knowledge in the cerebral palsy knowledge graph. Then, we embed the knowledge into tensors and inject them into the RNN. In addition, the KBRNN can benefit from the labeled EMRs. We use EMRs to fine-tune the KBRNN, which improves prediction accuracy. Our study shows that knowledge injection can effectively improve the model effect. The KBRNN can achieve 79.31% diagnostic accuracy with only knowledge injection. Moreover, the KBRNN can be further trained by the EMRs. The results show that the accuracy of fully trained KBRNN is 83.12%.
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11
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Zhu H, Wang R, Hua H, Qian H, Du P. Deciphering the potential role of Maca compounds prescription influencing gut microbiota in the management of exercise-induced fatigue by integrative genomic analysis. Front Nutr 2022; 9:1004174. [PMID: 36313119 PMCID: PMC9597638 DOI: 10.3389/fnut.2022.1004174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 09/27/2022] [Indexed: 11/24/2022] Open
Abstract
A growing number of nutraceuticals and cosmeceuticals have been utilized for millennia as anti-fatigue supplements in folk medicine. However, the anti-fatigue mechanism underlying is still far from being clearly explained. The aim of the study is to explore the underlying mechanism of the Maca compound preparation (MCP), a prescription for management of exercise-induced fatigue. In this study, mice weight-loaded swimming test was used to evaluate the anti-fatigue effect of MCP. MCP significantly improved the forelimb grip strength and Rota-rod test in behavioral tests via regulating energy metabolism. 16S rDNA sequencing results showed MCP can regulate the intestinal flora at the genus level by increasing several beneficial bacteria (i.e., Lactobacillus, Akkermansia and etc.), and decreasing the harmful bacteria (i.e., Candidatus_Planktophila and Candidatus_Arthromitus), where notable high relevance was observed between the fatigue-related biomarkers and fecal microbiota. The results of microbial function analysis suggested that MCP might improve exercise-induced fatigue by enhancing energy metabolism, carbohydrate and lipid metabolism and metabolism of terpenoids and polyketides and breakdown of amino acid metabolism. In addition, and H2O2-induced oxidative stress model on C2C12 cells was employed to further validate the regulation of MCP on energy metabolisms. MCP pre-treatment significantly reduced intracellular ROS accumulation, and increased glycogen content, ATP generation capacity and mitochondrial membrane potential of skeletal muscle cells, as well as conferred anti-cell necrosis ability. In conclusion, MCP plays a key role in regulating fatigue occurrence in exercising and gut microbiota balance, which may be of particular importance in the case of manual workers or sub-healthy populations.
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Affiliation(s)
- Hongkang Zhu
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China,Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, Wuxi, Jiangsu, China,School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
| | | | - Hanyi Hua
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China,Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, Wuxi, Jiangsu, China,School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
| | - He Qian
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China,Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, Wuxi, Jiangsu, China,School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China,*Correspondence: He Qian, amtf168168126.com
| | - Peng Du
- Air Force Medical Center, Beijing, China,Peng Du,
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12
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Wang S, Hou Y, Li X, Meng X, Zhang Y, Wang X. Practical Implementation of Artificial Intelligence-Based Deep Learning and Cloud Computing on the Application of Traditional Medicine and Western Medicine in the Diagnosis and Treatment of Rheumatoid Arthritis. Front Pharmacol 2022; 12:765435. [PMID: 35002704 PMCID: PMC8733656 DOI: 10.3389/fphar.2021.765435] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 12/09/2021] [Indexed: 12/23/2022] Open
Abstract
Rheumatoid arthritis (RA), an autoimmune disease of unknown etiology, is a serious threat to the health of middle-aged and elderly people. Although western medicine, traditional medicine such as traditional Chinese medicine, Tibetan medicine and other ethnic medicine have shown certain advantages in the diagnosis and treatment of RA, there are still some practical shortcomings, such as delayed diagnosis, improper treatment scheme and unclear drug mechanism. At present, the applications of artificial intelligence (AI)-based deep learning and cloud computing has aroused wide attention in the medical and health field, especially in screening potential active ingredients, targets and action pathways of single drugs or prescriptions in traditional medicine and optimizing disease diagnosis and treatment models. Integrated information and analysis of RA patients based on AI and medical big data will unquestionably benefit more RA patients worldwide. In this review, we mainly elaborated the application status and prospect of AI-assisted deep learning and cloud computation-oriented western medicine and traditional medicine on the diagnosis and treatment of RA in different stages. It can be predicted that with the help of AI, more pharmacological mechanisms of effective ethnic drugs against RA will be elucidated and more accurate solutions will be provided for the treatment and diagnosis of RA in the future.
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Affiliation(s)
- Shaohui Wang
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Ya Hou
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Xuanhao Li
- Chengdu Second People's Hospital, Chengdu, China
| | - Xianli Meng
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yi Zhang
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Xiaobo Wang
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
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Zhou S, Li K, Ogihara A, Wang X. Perceptions of traditional Chinese medicine doctors about using wearable devices and traditional Chinese medicine diagnostic instruments: A mixed-methodology study. Digit Health 2022; 8:20552076221102246. [PMID: 35646381 PMCID: PMC9134401 DOI: 10.1177/20552076221102246] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 05/05/2022] [Indexed: 02/06/2023] Open
Abstract
Objective This study aimed to investigate the perceptions of traditional Chinese
medicine doctors about wearable devices and diagnostic instruments and
explore the factors that influence them. Methods Data on the perceptions of the traditional Chinese medicine doctors in
Hangzhou, China, about wearable devices and diagnostic instruments were
collected through face-to-face semi-structured interviews. The author coded
the interview responses using grounded theory. A cross-sectional survey was
conducted in four traditional Chinese medicine hospitals in Hangzhou, China.
The responses of 385 traditional Chinese medicine doctors were considered
valid. Descriptive statistics and binary logistic regression models were
used for analysis. Results This study categorized the perceptions of traditional Chinese medicine about
wearable devices and traditional Chinese medicine diagnostic instruments
under convenience, reliability, suitable population, machine usage scenario,
and the integration of traditional Chinese medicine and information
communication technology. Convenience encompassed portability and the
convenience of carrying instruments or wearing the devices and operating
them and the human–device interface. Reliability encompassed the underlying
principles, accuracy, durability, and reference to diagnosis. Suitability
for people encompassed age distinction and disease differentiation. Machine
usage scenarios included use in daily life, educational institutions, and
primary medical institutions. The combination of traditional Chinese
medicine and information communication technology encompassed the
integration of traditional Chinese medicine and wearable functions and
diagnostic interpretation. The perceptions of traditional Chinese medicine
doctors were affected by age, title, type of hospital, and specialty. Conclusions The use of wearable devices and traditional Chinese medicine diagnostic
instruments has gradually been accepted by traditional Chinese medicine
doctors. Traditional Chinese medicine doctors need to improve their
knowledge and skills for information communication technology integration,
and their standardized training should incorporate information communication
technology and digital health.
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Affiliation(s)
- Siyu Zhou
- School of Public health, Hangzhou Normal University, Hangzhou, China
| | - Kai Li
- School of Medical technology, Zhejiang Chinese Medical University, Hangzhou, China
| | - Astushi Ogihara
- Department of Health Sciences and Social Welfare, Faculty of Human Sciences, Waseda University, Tokorozawa, Japan
| | - Xiaohe Wang
- School of Public health, Hangzhou Normal University, Hangzhou, China
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14
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Duan YY, Liu PR, Huo TT, Liu SX, Ye S, Ye ZW. Application and Development of Intelligent Medicine in Traditional Chinese Medicine. Curr Med Sci 2021; 41:1116-1122. [PMID: 34881423 PMCID: PMC8654490 DOI: 10.1007/s11596-021-2483-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Indexed: 01/16/2023]
Abstract
As modern science and technology constantly progresses, the fields of artificial intelligence, mixed reality technology, remote technology, etc. have rapidly developed. Meanwhile, these technologies have been gradually applied to the medical field, leading to the development of intelligent medicine. What’s more, intelligent medicine has greatly promoted the development of traditional Chinese medicine (TCM), causing huge changes in the diagnosis of TCM ailments, remote treatment, teaching, etc. Therefore, there are both opportunities and challenges for inheriting and developing TCM. Herein, the related research progress of intelligent medicine in the TCM in China and abroad over the years is analyzed, with the purpose of introducing the present application status of intelligent medicine in TCM and providing reference for the inheritance and development of TCM in a new era.
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Affiliation(s)
- Yu-Yu Duan
- Hubei University of Chinese Medicine, Wuhan, 430072, China
| | - Peng-Ran Liu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Tong-Tong Huo
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Song-Xiang Liu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Song Ye
- Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, 430060, China.
| | - Zhe-Wei Ye
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
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15
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Wang N, Liu Y, Jia C, Gao C, Zheng T, Wu M, Zhang Q, Zhao X, Li Z, Chen J, Wu C. Machine learning enables discovery of Gentianine targeting TLR4/NF-κB pathway to repair ischemic stroke injury. Pharmacol Res 2021; 173:105913. [PMID: 34563661 DOI: 10.1016/j.phrs.2021.105913] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 09/14/2021] [Accepted: 09/21/2021] [Indexed: 02/07/2023]
Abstract
Inflammatory response is believed to accelerate the development of stroke injury. Gentianine, an alkaloid isolated from Gentiana Scabra Bunge, shows effectiveness in anti-inflammation. In this study, the effect of Gentianine on transient middle cerebral artery occlusion (tMCAO) induced mouse model in vivo and further related mechanism in LPS-injuried microglia BV-2 cells in vitro were explored. Effect of Gentianine on tMCAO mouse demonstrated that Gentianine significantly ameliorated tMCAO induced ischemic injury by decreasing brain infarct volume and increasing the neurological score and upper limb muscle strength. Meanwhile, Gentianine significantly decreased the release of serum inflammatory cytokines. Machine learning enables that Gentianine might had anti-ischemic stroke effect through the TLR4/NF-κB signaling pathway. This was verified in vivo and in vitro. Gentianine significantly decrease the TLR4 and Iba-1 expression in vivo. These results also verified in BV-2 cells. Gentianine significantly decreased TLR4, MyD88 and NF-κB expression, as well as NO production and inflammatory cytokines release. Gentianine co-treatment with TLR4 inhibitor, further decreased TLR4, MyD88 and NF-κB expression, NO production, as well as the inflammatory cytokines. Taken together, Gentianine could be used as a potential anti-ischemic stroke agent by suppressing inflammatory responses via TLR4/NF-κB signaling pathway. This study is expected to provide an integrated traditional Chinese and western medicine solution to find potential anti-ischemic stroke compounds based on machine learning.
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Affiliation(s)
- Na Wang
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
| | - Yao Liu
- The Affiliated Hospital of Qingdao University and Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao, China
| | - Caixia Jia
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
| | - Chengwen Gao
- The Affiliated Hospital of Qingdao University and Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao, China
| | | | - Mingxuan Wu
- The Affiliated Hospital of Qingdao University and Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao, China
| | - Qian Zhang
- The Affiliated Hospital of Qingdao University and Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao, China
| | - Xiangzhong Zhao
- The Affiliated Hospital of Qingdao University and Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao, China
| | - Zhiqiang Li
- The Affiliated Hospital of Qingdao University and Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao, China.
| | - Jianxin Chen
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
| | - Chuanhong Wu
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China; The Affiliated Hospital of Qingdao University and Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao, China.
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