Application of Bayesian network in information fusion analysis of four diagnostic methods of traditional Chinese medicine

2010 ◽  
Author(s):  
Wenjie Xu ◽  
Yiqin Wang ◽  
Zhaoxia Xu ◽  
Chunfeng Chen ◽  
Xiaojuan Zou
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Bayesian Network ◽  
Information Fusion ◽  
Diagnostic Methods ◽  
Fusion Analysis

scholarly journals Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Changbo Zhao ◽  
Guo-Zheng Li ◽  
Chengjun Wang ◽  
Jinling Niu
Keyword(s):  
Machine Learning ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Machine Learning Algorithms ◽  
Diagnostic Methods ◽  
Machine Learning Techniques ◽  
Syndrome Differentiation ◽  
Reference List ◽  
Patient Classification ◽  
Research Issues

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification.

scholarly journals Effective Heart Disease Detection Based on Quantitative Computerized Traditional Chinese Medicine Using Representation Based Classifiers

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Ting Shu ◽  
Bob Zhang ◽  
Yuan Yan Tang
Keyword(s):  
Heart Disease ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Diagnostic Methods ◽  
Collaborative Representation ◽  
Disease Detection ◽  
Color Analysis ◽  
Key Block ◽  
Facial Images ◽  
Tomography Scan

At present, heart disease is the number one cause of death worldwide. Traditionally, heart disease is commonly detected using blood tests, electrocardiogram, cardiac computerized tomography scan, cardiac magnetic resonance imaging, and so on. However, these traditional diagnostic methods are time consuming and/or invasive. In this paper, we propose an effective noninvasive computerized method based on facial images to quantitatively detect heart disease. Specifically, facial key block color features are extracted from facial images and analyzed using the Probabilistic Collaborative Representation Based Classifier. The idea of facial key block color analysis is founded in Traditional Chinese Medicine. A new dataset consisting of 581 heart disease and 581 healthy samples was experimented by the proposed method. In order to optimize the Probabilistic Collaborative Representation Based Classifier, an analysis of its parameters was performed. According to the experimental results, the proposed method obtains the highest accuracy compared with other classifiers and is proven to be effective at heart disease detection.

scholarly journals Determination of the Type of Heart Syndrome in Traditional Chinese Medicine with the Bayesian Network Method

2020 ◽  
Vol 12 (2) ◽  
pp. 32-38
Author(s):  
Asto Buditjahjanto
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Expert Knowledge ◽  
Prior Probabilities ◽  
Network Method

The determination of a disease syndrome in the TCM is difficult enough to determine because it requires a lot of experience in observing patients' symptoms that appear in disease syndrome and their disease syndrome history. Symptoms that appear in one disease syndrome are varied and can also appear in other disease syndromes. This research limits the determination of the type of syndrome only in the heart organ. The purpose of this study is to determine the type of heart syndrome in TCM by using Bayesian Networks. Bayesian Networks is used because it has the advantage of adapting expert knowledge toward the preferences of symptoms that arise at a type of heart syndrome. The expert's preference is in the weights that act as prior probabilities that are used as the basis for calculations on the Bayesian Networks. The results showed that the Bayesian Networks can be used to determine the type of heart syndrome well. The results of trials on 7 patients yield the same diagnosis between the doctor's diagnosis and the Bayesian Networks calculation

scholarly journals A Cross-Sectional Study of Traditional Chinese Medicine Practitioner’s Knowledge, Treatment Strategies and Integration of Practice of Chronic Pelvic Pain in Women

2021 ◽  
Author(s):  
Susan Arentz ◽  
Caroline Smith ◽  
Rebecca Redmond ◽  
Jason Abbott ◽  
Mike Armour
Keyword(s):  
Health Care ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Health Care Providers ◽  
Pelvic Pain ◽  
Chronic Pelvic Pain ◽  
Clinical Decision Making ◽  
Diagnostic Methods ◽  
Care Providers ◽  
Cross Sectional

Abstract Background Chronic pelvic pain (CPP) in women is persistent, intermittent cyclical and non-cyclical lower abdominal pain, lasting for more than 6 months. Traditional Chinese Medicine (TCM) is a popular treatment option for women’s health conditions, but little is known about how treatment for CPP is delivered by TCM practitioners. The aim of this survey was to explore practitioners understanding and treatment of women with CPP, and how they integrate their management and care into the health care system. Method An online cross-sectional survey of registered TCM practitioners in Australia and New Zealand. Survey domains included treatment characteristics (e.g. frequency), evaluation of treatment efficacy, referral networks, and sources of information that informed clinical decision making. Results One hundred and twenty-two registered TCM practitioners responded to this survey, 91.7% reported regular treatment of women with CPP. Treatment decisions were most-often guided by a combination of biomedical and TCM diagnosis (77.6%), and once per week was the most common treatment frequency (66.7%) for acupuncture. Meditation (63.7%) and dietary changes (57.8%) were other commonly used approaches to management. The effectiveness of treatment was assessed using multiple approaches, most commonly pain scales, (such as the numeric rating scale) and any change in use of analgesic medications. Limitations to TCM treatment were reported by over three quarters (83.7%) of practitioners, most commonly due to cost (56.5%) and inconvenience (40.2%) rather than safety or lack of efficacy. Integration within the wider healthcare system was common with over two thirds (67.9%) receiving referrals from health care providers. Conclusion TCM practitioners seeing women with various CPP symptoms, commonly incorporate both traditional and modern diagnostic methods to inform their treatment plan, monitor treatment progress using commonly accepted approaches and measures and often as a part of multidisciplinary healthcare for women with CPP.

The Impact of Artificial Intelligence on Traditional Chinese Medicine

2021 ◽  
pp. 1-18
Author(s):  
Yulin Wang ◽  
Xiuming Shi ◽  
Li Li ◽  
Thomas Efferth ◽  
Dong Shang
Keyword(s):  
Artificial Intelligence ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Modern Science ◽  
Diagnostic Methods ◽  
Digital Data ◽  
Syndrome Differentiation ◽  
Efficacy Evaluation ◽  
Combination Rules ◽  
The Impact

Traditional Chinese Medicine (TCM) is a well-established medical system with a long history. Currently, artificial intelligence (AI) is rapidly expanding in many fields including TCM. AI will significantly improve the reliability and accuracy of diagnostics, thus increasing the use of effective therapeutic methods for patients. This systematic review provides an updated overview on the major breakthroughs in the field of AI-assisted TCM four diagnostic methods, syndrome differentiation, and treatment. AI-assisted TCM diagnosis is mainly based on digital data collected by modern electronic instruments, which makes TCM diagnosis more quantitative, objective, and standardized. As a result, the diagnosis decisions made by different TCM doctors exhibit more consistency, accuracy, and reliability. Meanwhile, the therapeutic efficacy of TCM can be evaluated objectively. Therefore, AI is promoting TCM from experience to evidence-based medicine, a genuine scientific revolution. Furthermore, huge and non-uniform knowledge on formula-syndrome relationships and the combination rules of herbal TCM formulae could be better standardized with the help of AI analysis, which is necessary for the clinical efficacy evaluation and further optimization on the standardized TCM formulae. AI bridges the gap between TCM and modern science and technology. AI may bring clinical TCM diagnostics closer to western medicine. With the help of AI, more scientific evidence about TCM will be discovered. It can be expected that more unified guidelines for specific TCM syndromes will be issued with the development of AI-assisted TCM therapies in the future.

Analysis Methods of Use Acupuncture Diagnostics in Multiple Sclerosis Patients Under Comorbidity

2016 ◽  
pp. 60-64
Author(s):  
Hennadii Chupryna ◽  
Nataliia Svyrydova ◽  
Anatolii Galusha
Keyword(s):  
Multiple Sclerosis ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Pathological Process ◽  
Diagnostic Methods ◽  
Complex Treatment ◽  
Liver And Kidney ◽  
Qi Stagnation ◽  
Multiple Sclerosis Patients ◽  
Kidney Liver

When optimizing the use of acupuncture methods in the complex treatment of patients with MS, important individual approach to treatment is based on a differentiated compilation of recipes acupuncture, built according to the diagnostic data obtained on the basis of current scientific knowledge and understanding of traditional Chinese medicine (TCM) The objective: to determine the features of these acupuncture diagnostic methods (ADM) in MS patients based on comorbidity and clarify their relationship with the clinical neurological symptoms of MS patients examined. Patients and methods. ADM was conducted in 216 patients with MS who were divided into 2 groups: without concomitant diseases (n=109) and the presence of concomitant diseases (n=107). Results. When conducting syndromic ADM according to TCM, in all patients (100%) of us was diagnosed three dominant syndrome of TCM, which we have identified as the main: «spleen qi deficiency», «deficiency of liver and kidney yin», «liver qi stagnation». Conclusions. As a result of multi-component ADM MS patients were identified pre-emptive involvement in the pathological process of functional systems TCM «Spleen-pancreas», «Kidney», «Liver».

scholarly journals Seni Mendeteksi Penyakit Melalui Lidah Dalam Budaya Pengobatan Tradisional Tiongkok

2021 ◽  
Vol 4 (1) ◽  
pp. 11
Author(s):  
Ni Made Sinarsari ◽  
I Gede Sutana
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Traditional Medicine ◽  
Visual Inspection ◽  
The Body ◽  
Diagnostic Methods ◽  
Internal Organs ◽  
Disease Patterns ◽  
Inspection Of The Tongue ◽  
Important Indication

<p><em>Traditional medicine is closely related to the culture in which it develops. The art of detecting disease through the tongue is one of the main diagnostic methods in Traditional Chinese Medicine culture that has been applied since the classical times of the Yellow Emperor. Tongue diagnosis in Traditional Chinese Medicine culture is a method for diagnosing disease and disease patterns by visual inspection of the tongue and its various features. As with other diagnostic methods, the diagnosis of the tongue is based on the principle that the outside of the body reflects the inside of the body. The tongue provides important clues that reflect the condition of the internal organs in each patient. In Traditional Chinese Medicine culture, the external structure of the body often reflects the condition of the inner structure and can give an important indication of internal disharmony.</em></p>

scholarly journals Research on the diagnosis and treatment of the dominant diseases of traditional Chinese medicine based on machine learning

2020 ◽  
Author(s):  
yuqi tang ◽  
Dongdong Yang ◽  
Zechen Li ◽  
Yu Fang ◽  
Shanshan Gao
Keyword(s):  
Machine Learning ◽  
Chinese Medicine ◽  
Random Forest ◽  
Traditional Chinese Medicine ◽  
Hierarchical Cluster ◽  
Diagnostic Methods ◽  
Diagnosis And Treatment ◽  
Syndrome Differentiation ◽  
The Novel ◽  
Sample Data

Abstract Background: Insomnia as one of the dominant diseases of traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning. Methods: First of all, 654 insomnia cases have been collected from an experienced doctor of TCM as sample data. Secondly, in the light of the characteristics of TCM diagnosis and treatment, the contents of research samples have been divided into four parts: the basic information, the four diagnostic methods, the treatment based on syndrome differentiation and the main prescription. And then, these four parts have been analyzed by three analysis methods, including frequency analysis, association rules and hierarchical cluster analysis. Finally, a comprehensive study of the whole four parts has been conducted by random forest. Results: Researches of the above four parts revealed some essential connections. Simultaneously, based on the algorithm model established by the random forest, the accuracy of predicting the main prescription by the combination of the four diagnostic methods and the treatment based on syndrome differentiation was 0.85. Furthermore, having been extracted features through applying the random forest, the syndrome differentiation of five zang-organs was proven to be the most significant parameter of the TCM diagnosis and treatment.Conclusions: The results indicate that the machine learning methods are worthy of being adopted to study the dominant diseases of TCM for exploring the crucial rules of the diagnosis and treatment.

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