Knowledge graph for TCM health preservation: Design, construction, and applications

Yu, Tong; Li, Jinghua; Yu, Qi; Tian, Yuan; Shun, Xiaofeng; Xu, Lili; Zhu, Liling; Gao, Hongjie

doi:10.1016/j.artmed.2017.04.001

Cited by 127 publications

(69 citation statements)

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“…1) Collect more data (not only for tongue images) and then verify and improve the performance of the proposed models on larger and richer datasets. 2) Absorb more medicine transcendental knowledge or ontology [35], [47] to guide the neural prescription construction models. 3) Invite experienced Chinese doctors to provide a more professional artificial score for the generated prescription, which would introduce the online reinforcement learning mechanism into the basic system.…”

Section: Discussionmentioning

confidence: 99%

Automatic Construction of Chinese Herbal Prescriptions From Tongue Images Using CNNs and Auxiliary Latent Therapy Topics

Wen

Liao

et al. 2021

IEEE Trans. Cybern.

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The tongue image provides important physical information of humans. It is of great importance for diagnoses and treatments in clinical medicine. Herbal prescriptions are simple, noninvasive, and have low side effects. Thus, they are widely applied in China. Studies on the automatic construction technology of herbal prescriptions based on tongue images have great significance for deep learning to explore the relevance of tongue images for herbal prescriptions, it can be applied to healthcare services in mobile medical systems. In order to adapt to the tongue image in a variety of photographic environments and construct herbal prescriptions, a neural network framework for prescription construction is designed. It includes single/double convolution channels and fully connected layers. Furthermore, it proposes the auxiliary therapy topic loss mechanism to model the therapy of Chinese doctors and alleviate the interference of sparse output labels on the diversity of results. The experiment use the real-world tongue images and the corresponding prescriptions and the results can generate prescriptions that are close to the real samples, which verifies the feasibility of the proposed method for the automatic construction of herbal prescriptions from tongue images. Also, it provides a reference for automatic herbal prescription construction from more physical information.

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Section: Discussionmentioning

confidence: 99%

Automatic Construction of Chinese Herbal Prescriptions From Tongue Images Using CNNs and Auxiliary Latent Therapy Topics

Wen

Liao

et al. 2021

IEEE Trans. Cybern.

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“…The artificial intelligence model proposed in this paper is based on Ontology that considers the essence of a certain entity as a combination of several fundamental attributions with corresponding values and relationships [2,23,24]. Such attributions are not only the definite properties which are already completely recognized by researchers, but also the latent properties including unknown information and relationships.…”

Section: The Ontology-based Drug Modelmentioning

confidence: 99%

“…Artificial intelligence is a modern technology that is utilized in various fields of medicine [1][2][3]. At the meantime, Chinese Traditional Medicine (TCM) is now widely considered as a promising alternative medicine for complementary treatment in cancers or chronic diseases due to the effective methodology practically developed by generations of doctors for almost 4000 years [4].…”

Section: Introductionmentioning

confidence: 99%

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

Yao

Wang

et al. 2019

Computational and Mathematical Methods in Medicine

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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 the proposed model are presented. To validate the proposed model, an ANN structure is established and trained by two hundred 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.

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“…Some studies used topic model to discover prescribing patterns [4,26]. There are other studies such as TCM medicine classification and recognition [27,28], knowledge graph [29,30] for TCM.…”

Section: Related Workmentioning

confidence: 99%

Convolutional herbal prescription building method from multi-scale facial features

Liao

Wen

et al. 2019

Multimed Tools Appl

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In Traditional Chinese Medicine (TCM), facial features are important basis for diagnosis and treatment. A doctor of TCM can prescribe according to a patient's physical indicators such as face, tongue, voice, symptoms, pulse. Previous works analyze and generate prescription according to symptoms. However, research work to mine the association between facial features and prescriptions has not been found for the time being. In this work, we try to use deep learning methods to mine the relationship between the patient's face and herbal prescriptions (TCM prescriptions), and propose to construct convolutional neural networks that generate TCM prescriptions according to the patient's face image. It is a novel and challenging job. In order to mine features from different granularities of faces, we design a multi-scale convolutional neural network based on three-grained face, which mines the patient's face information from the organs, local regions, and the entire face. Our experiments show that convolutional neural networks can learn relevant information from face to prescribe, and the multi-scale convolutional neural networks based on three-grained face perform better.

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Knowledge graph for TCM health preservation: Design, construction, and applications

Cited by 127 publications

References 0 publications

Automatic Construction of Chinese Herbal Prescriptions From Tongue Images Using CNNs and Auxiliary Latent Therapy Topics

Automatic Construction of Chinese Herbal Prescriptions From Tongue Images Using CNNs and Auxiliary Latent Therapy Topics

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

Convolutional herbal prescription building method from multi-scale facial features

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