An approach to syndrome differentiation in traditional chinese medicine based on neural network

Although the traditional knowledge representation based on rules is simple and explicit, it is not effective in the field of syndrome differentiation in Traditional Chinese Medicine (TCM), which involves many uncertain concepts. To represent uncertain knowledge of syndrome differentiation in TCM, two methods were presented respectively based on certainty factors and certainty intervals. Exploiting these two methods, an approach to syndrome differentiation in TCM was proposed based on neural networks to avoid some limitations of other approaches. The main advantage of the approach is that it may realize uncertain inference of syndrome differentiation in TCM, whereas it doesn't request experts to provide all possible combinations for certainty degrees of symptoms and syndromes. Rather than Back Propagation (BP) algorithm but its modification was employed to improve the capability of generalization of neural networks. First, the standard feedforward multilayer BP neural network and its modification were introduced. Next, two methods for knowledge representation, respectively based on certainty factors and certainty intervals, were presented Then, the algorithm was proposed based on neural network for the uncertain inference of syndrome differentiation in TCM. Finally, an example was demonstrated to illustrate the algorithm

An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as An Example

In this work, an ontology-based model for AI-assisted medicine side-effect (SE) prediction is developed, where three main components, including the drug model, the treatment model, and the AI-assisted prediction model, of proposed model are presented. To validate the proposed model, an ANN structure is established and trained by two hundred and forty-two TCM prescriptions. These data are gathered and classified from the most famous ancient TCM book and more than one thousand SE reports, in which two ontology-based attributions, hot and cold, are introduced to evaluate whether the prescription will cause SE or not. The results preliminarily reveal that it is a relationship between the ontology-based attributions and the corresponding predicted indicator that can be learnt by AI for predicting the SE, which suggests the proposed model has a potential in AI-assisted SE prediction. However, it should be noted that, the proposed model highly depends on the sufficient clinic data, and hereby, much deeper exploration is important for enhancing the accuracy of the prediction

Combining Artificial Intelligence with Traditional Chinese Medicine for Intelligent Health Management

The growth of artificial intelligence (AI) is being referred to as the beginning of "the fourth industrial revolution". With the rapid development of hardware, algorithms, and applications, AI not only provides a new concept and relevant solutions to solve the problem of complexity science but also provides a new concept and method to promote the development of traditional Chinese medicine (TCM). In this study, based on the research and development of AI technology applications in biomedical and clinical diagnosis and treatment, we introduce AI technologies in current TCM research. This can have applications in intelligent clinical information acquisition, intelligent clinical decision, and efficacy evaluation of TCM; intelligent classification management, intelligent prescription, and drug research in Chinese herbal medicine; and health management. Furthermore, we propose a framework of "intelligent TCM" and outline its development prospects

Targeting neurogenesis: a promising therapeutic strategy for post-stroke treatment with Chinese herbal medicine

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Modelling of inquiry diagnosis for coronary heart disease in traditional Chinese medicine by using multi-label learning

<p>Abstract</p> <p>Background</p> <p>Coronary heart disease (CHD) is a common cardiovascular disease that is extremely harmful to humans. In Traditional Chinese Medicine (TCM), the diagnosis and treatment of CHD have a long history and ample experience. However, the non-standard inquiry information influences the diagnosis and treatment in TCM to a certain extent. In this paper, we study the standardization of inquiry information in the diagnosis of CHD and design a diagnostic model to provide methodological reference for the construction of quantization diagnosis for syndromes of CHD. In the diagnosis of CHD in TCM, there could be several patterns of syndromes for one patient, while the conventional single label data mining techniques could only build one model at a time. Here a novel multi-label learning (MLL) technique is explored to solve this problem.</p> <p>Methods</p> <p>Standardization scale on inquiry diagnosis for CHD in TCM is designed, and the inquiry diagnostic model is constructed based on collected data by the MLL techniques. In this study, one popular MLL algorithm, ML-kNN, is compared with other two MLL algorithms RankSVM and BPMLL as well as one commonly used single learning algorithm, k-nearest neighbour (kNN) algorithm. Furthermore the influence of symptom selection to the diagnostic model is investigated. After the symptoms are removed by their frequency from low to high; the diagnostic models are constructed on the remained symptom subsets.</p> <p>Results</p> <p>A total of 555 cases are collected for the modelling of inquiry diagnosis of CHD. The patients are diagnosed clinically by fusing inspection, pulse feeling, palpation and the standardized inquiry information. Models of six syndromes are constructed by ML-kNN, RankSVM, BPMLL and kNN, whose mean results of accuracy of diagnosis reach 77%, 71%, 75% and 74% respectively. After removing symptoms of low frequencies, the mean accuracy results of modelling by ML-kNN, RankSVM, BPMLL and kNN reach 78%, 73%, 75% and 76% when 52 symptoms are remained.</p> <p>Conclusions</p> <p>The novel MLL techniques facilitate building standardized inquiry models in CHD diagnosis and show a practical approach to solve the problem of labelling multi-syndromes simultaneously.</p

Applications of New Technologies and New Methods in ZHENG Differentiation

With the hope to provide an effective approach for personalized diagnosis and treatment clinically, Traditional Chinese Medicine (TCM) is being paid increasing attention as a complementary and alternative medicine. It performs treatment based on ZHENG (TCM syndrome) differentiation, which could be identified as clinical special phenotypes by symptoms and signs of patients. However, it caused skepticism and criticism because ZHENG classification only depends on observation, knowledge, and clinical experience of TCM practitioners, which is lack of objectivity and repeatability. Scientists have done fruitful researches for its objectivity and standardization. Compared with traditional four diagnostic methods (looking, listening and smelling, asking, and touching), in this paper, the applications of new technologies and new methods on the ZHENG differentiation were systemically reviewed, including acquisition, analysis, and integration of clinical data or information. Furthermore, the characteristics and application range of these technologies and methods were summarized. It will provide reference for further researches