Using Traditional Chinese Medicine Ideas as a Mechanism to Engage People in Health Awareness

Song, Donglei; Xia, Yu; Wang, Rusi; Xu, Hao

doi:10.3390/su10082702

Cited by 9 publications

(6 citation statements)

References 15 publications

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“…3) Improve health management of Traditional Chinese Medical with fuzzy deep learning. Traditional Chinese Medical health management (Song, Xia et al 2018) uses the core idea of cure not ill, overall concept, and syndrome differentiation, to provide healthy people or sub-health population with Traditional Chinese Medical health consultation, guidance, diagnosis and treatment. The effect of Traditional Chinese medical has been scientifically proven and makes great differences during the treatment process of COVID-19 (Li, Hou et al 2020) (Li, Wei et al 2021).…”

Section: Future Research Directionsmentioning

confidence: 99%

A Systematic Survey of Fuzzy Deep Learning for Uncertain Medical Data

Zheng

Zhang

et al. 2023

Preprint

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Intelligent medical industry is in a rapid stage of development around the world, followed by are the expanding market size and basic theories of intelligent medical diagnosis and decision-making. Deep learning models have achieved good practical results in medical domain. However, traditional deep learning is almost calculated and developed by crisp values, while imprecise, uncertain, and vague medical data is common in the process of diagnosis and treatment. Medical data is usually uncertain because of the presence of noise, artifact or high dimensional unstructured information in the data. Fortunately, fuzzy deep learning that originated from fuzzy sets, can effectively deal with uncertain and inaccurate information, providing new viewpoints for alleviating the problems above. Therefore, it is important and necessary to review the contributions of fuzzy deep learning for uncertain medical data in recent years. This paper first constructs four types of frameworks of fuzzy deep learning methods used for uncertain medical data, and investigates the status from three aspects: fuzzy deep learning methods, type of uncertain medical data and execution effects. Then the performance evaluation metrics of fuzzy deep learning models are analyzed in details. We have found that although fuzzy deep learning indeed develops the interpretability, low-quality images, boundary point processing, imprecise text records processing and multi-source heterogenous data fusion in medical domain, challenges still exist and future research directions are provided from the perspective of genetic data processing, intelligent medical decision-making based on complex cognition information, health management of Traditional Chinese Medical and few-shot learning.

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Section: Future Research Directionsmentioning

confidence: 99%

A Systematic Survey of Fuzzy Deep Learning for Uncertain Medical Data

Zheng

Zhang

et al. 2023

Preprint

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“…There are some studies aimed at improving healthcare publicity. In (16), traditional Chinese medicinebased simplified health management application was proposed to help the healthcare management of people. In (17), the authors constructed some areas of healthcare technology research and support practitioners in healthcare technology design, development, selection, and acquisition.…”

Section: Related Studymentioning

confidence: 99%

AI-Based Publicity Strategies for Medical Colleges: A Case Study of Healthcare Analysis

Wang

2022

Front. Public Health

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The health status and cognition of undergraduates, especially the scientific concept of healthcare, are particularly important for the overall development of society and themselves. The survey shows that there is a significant lack of knowledge about healthcare among undergraduates in medical college, even among medical undergraduates, not to mention non-medical undergraduates. Therefore, it is a good way to publicize healthcare lectures or electives for undergraduates in medical college, which can strengthen undergraduates' cognition of healthcare and strengthen the concept of healthcare. In addition, undergraduates' emotional and mental state in healthcare lectures or electives can be analyzed to determine whether undergraduates have hidden illnesses and how well they understand the healthcare content. In this study, at first, a mental state recognition method of undergraduates in medical college based on data mining technology is proposed. Then, the vision-based expression and posture are used for expanding the channels of emotion recognition, and a dual-channel emotion recognition model based on artificial intelligence (AI) during healthcare lectures or electives in a medical college is proposed. Finally, the simulation is driven by TensorFlow with respect to mental state recognition of undergraduates in medical college and emotion recognition. The simulation results show that the recognition accuracy of mental state recognition of undergraduates in a medical college is more than 92%, and the rejection rate and misrecognition rate are very low, and false match rate and false non-match rate of mental state recognition is significantly better than the other three benchmarks. The emotion recognition of the dual-channel emotion recognition method is over 96%, which effectively integrates the emotional information expressed by facial expressions and postures.

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“…In addition, another function of this system is to make personal food recommendations for users. When first using this system, users fill in some questionnaires and upload photos of their tongue [12]. Based on the questionnaires, the system can consider the user's conditions and make personal recommendations.…”

Section: Retrieval Based On Knowledge Graphmentioning

confidence: 99%

“…Finally, based on the knowledge graph, a semantic retrieval application is implemented, which is aimed at helping users who do not have a background in related fields to efficiently and comprehensively retrieve and analyze healthy diet knowledge. Meanwhile, the system can automatically classify users' physique types based on their tongue phase classification [12] and provide personalized food recommendations. In the evaluation, 90 experimenters used the prototype system for 30 days, and their feedback on a questionnaire was analyzed.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Management in Healthcare Sustainability: A Smart Healthy Diet Assistant in Traditional Chinese Medicine Culture

Yang

et al. 2018

Sustainability

Self Cite

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In the past 40 years, with the changes to dietary structure and the dramatic increase in the consumption of meat products in developing countries, especially in China, encouraging populations to maintain their previous healthy eating patterns will have health, environmental, and economic co-benefits. Healthy diet education plays an important role in the promotion of people's healthy behavior. However, in the modern age, the data regarding healthy diets available on the internet is increasing rapidly and is distributed on multiple sources. It is time-consuming for users to learn about healthy diets on the internet: they need to search data on multiple platforms, choose and integrate information, and then understand what they have learned. To help people retrieve and learn healthy diet knowledge more efficiently and comprehensively, this paper designs a knowledge graph to integrate healthy diet information on the internet and provides a semantic retrieval system. In the knowledge graph, five main concepts are defined, including food material, dish, nutritional element, symptom, and crowd, as well as the relationships among them. In addition, Chinese dietary culture elements and traditional Chinese medicine (TCM) theory are also contained in the knowledge graph. The preliminary results show that by using the system, users learn healthy diet knowledge more quickly and comprehensively and they are more inclined to have balanced diets. This work could be regarded as a retrieval and education tool, which can assist healthcare and national sustainable development.

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Using Traditional Chinese Medicine Ideas as a Mechanism to Engage People in Health Awareness

Cited by 9 publications

References 15 publications

A Systematic Survey of Fuzzy Deep Learning for Uncertain Medical Data

A Systematic Survey of Fuzzy Deep Learning for Uncertain Medical Data

AI-Based Publicity Strategies for Medical Colleges: A Case Study of Healthcare Analysis

Knowledge Management in Healthcare Sustainability: A Smart Healthy Diet Assistant in Traditional Chinese Medicine Culture

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