Modeling of diagnosis for metabolic syndrome by integrating symptoms into physiochemical indexes

Xia, Shujie; Gao, Bizhen; Wang, Shuihua; Guttery, David S.; Li, Candong; Zhang, Yudong

doi:10.1016/j.biopha.2021.111367

Cited by 23 publications

(19 citation statements)

References 14 publications

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“…In the most recent stage. In addition to the previous research on the tongue, there were also more studies on the pulse and TCM symptoms (8,(23)(24)(25)(26)(27)(28).…”

Section: Discussionmentioning

confidence: 99%

Current status and trends of artificial intelligence research on the four traditional Chinese medicine diagnostic methods: a scientometric study

Tian¹,

Wang²,

Sun³

et al. 2023

Ann Transl Med

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Background: With the development of technology and the renewal of traditional Chinese medicine (TCM) diagnostic equipment, artificial intelligence (AI) has been widely applied in TCM. Numerous articles employing this technology have been published. This study aimed to outline the knowledge and themes trends of the four TCM diagnostic methods to help researchers quickly master the hotspots and trends in this field. Four TCM diagnostic methods is a TCM diagnostic method through inspection, listening, smelling, inquiring and palpation, the purpose of which is to collect the patient's medical history, symptoms and signs. Then, it provides an analytical basis for later disease diagnosis and treatment plans. Methods: Publications related to AI-based research on the four TCM diagnostic methods were selected from the Web of Science Core Collection, without any restriction on the year of publication. VOSviewer and Citespace were primarily used to create graphical bibliometric maps in this field. Results: China was the most productive country in this field, and Evidence-Based Complementary and Alternative Medicine published the largest number of related papers, and the Shanghai University of Traditional Chinese Medicine is the dominant research organization. The Chengdu University of Traditional Chinese Medicine had the highest average number of citations. Jinhong Guo was the most influential author and Artificial Intelligence in Medicine was the most authoritative journal. Six clusters separated by keywords association showed the range of AI-based research on the four TCM diagnostic methods. The hotspots of AI-based research on the four TCM diagnostic methods included the classification and diagnosis of tongue images in patients with diabetes and machine learning for TCM symptom differentiation.Conclusions: This study demonstrated that AI-based research on the four TCM diagnostic methods is currently in the initial stage of rapid development and has bright prospects. Cross-country and regional cooperation should be strengthened in the future. It is foreseeable that more related research outputs will rely on the interdisciplinarity of TCM and the development of neural networks models.

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“…In the most recent stage. In addition to the previous research on the tongue, there were also more studies on the pulse and TCM symptoms (8,(23)(24)(25)(26)(27)(28).…”

Section: Discussionmentioning

confidence: 99%

Current status and trends of artificial intelligence research on the four traditional Chinese medicine diagnostic methods: a scientometric study

Tian¹,

Wang²,

Sun³

et al. 2023

Ann Transl Med

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“…SVR is a kernel-based technique allowing to work with arbitrary large feature space and offers good generalization performance [ 23 ]. Moreover, these methods have been extensively used in many bioengineering applications including classification of cardiac diseases [ 24 ], cardiac abnormalities [ 25 ], diagnosis of diabetes [ 26 ], detection of Alzheimer’s disease [ 27 ], classification of metabolic diseases [ 28 ], and COVID-19 diagnosis [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

A Non-Invasive Hydration Monitoring Technique Using Microwave Transmission and Data-Driven Approaches

Agarwal

Randall

White

et al. 2022

Sensors

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Dehydration in the human body arises due to inadequate replenishment of fluids. An appropriate level of hydration is essential for optimal functioning of the human body, and complications ranging from mild discomfort to, in severe cases, death, could result from a neglected imbalance in fluid levels. Regular and accurate monitoring of hydration status can provide meaningful information for people operating in stressful environmental conditions, such as athletes, military professionals and the elderly. In this study, we propose a non-invasive hydration monitoring technique employing non-ionizing electromagnetic power in the microwave band to estimate the changes in the water content of the whole body. Specifically, we investigate changes in the attenuation coefficient in the frequency range 2–3.5 GHz between a pair of planar antennas positioned across a participant’s arm during various states of hydration. Twenty healthy young adults (10M, 10F) underwent controlled hypohydration and euhydration control bouts. The attenuation coefficient was compared among trials and used to predict changes in body mass. Volunteers lost 1.50±0.44% and 0.49±0.54% body mass during hypohydration and euhydration, respectively. The microwave transmission-based attenuation coefficient (2–3.5 GHz) was accurate in predicting changes in hydration status. The corresponding regression analysis demonstrates that building separate estimation models for dehydration and rehydration phases offer better predictive performance (88%) relative to a common model for both the phases (76%).

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“…For the same purpose, different models could exhibit different efficacy, such as how in supervised machine learning, the sensitivity and specificity vary among different models and detailed studies are needed to designate the most suitable model. For MetS, various algorithms have been tested, and many highlighted the “random forest” as the most appropriate model ( Xia et al, 2021 ; Yu et al, 2021 ), and studies have also used deep learning tools to analyze data from medical images to further contribute to the prediction of MetS occurrence and outcomes, contributing to secondary and tertiary prevention strategies ( Lin et al, 2021a ; Pickhardt et al, 2021 ).…”

Section: Common Data Sources and Analytic Tools For Big Data Research...mentioning

confidence: 99%

“…A similar study adopted a broader expansion for risk factor inclusion by incorporating concepts from traditional medicine, as conventional medicine has statistical significance via long-term accumulation of “medical data.” The study included the “Sasang constitution type” from traditional Korean medicine for comparison of six types of machine learning methods over a data set of 2,871 visitors from a medical center and discovered higher sensitivity for prediction with incorporation “Sasang constitution type” ( Park et al, 2021 ). Another study incorporating traditional medicine methodology combined clinical variables, including 20 physicochemical indexes commonly tested in routine medical practice, with 47 symptoms described within the framework of traditional Chinese medicine ( Xia et al, 2021 ). Three machine learning methods were tested with these data resulting in the superiority of the rain forest model, whose prediction power increased with the incorporation of symptom variables.…”

Section: Early Detection Of Mets: From “Omics” To Clinical “Big Data”...mentioning

confidence: 99%

“Big Data” Approaches for Prevention of the Metabolic Syndrome

et al. 2022

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Metabolic syndrome (MetS) is characterized by the concurrence of multiple metabolic disorders resulting in the increased risk of a variety of diseases related to disrupted metabolism homeostasis. The prevalence of MetS has reached a pandemic level worldwide. In recent years, extensive amount of data have been generated throughout the research targeted or related to the condition with techniques including high-throughput screening and artificial intelligence, and with these “big data”, the prevention of MetS could be pushed to an earlier stage with different data source, data mining tools and analytic tools at different levels. In this review we briefly summarize the recent advances in the study of “big data” applications in the three-level disease prevention for MetS, and illustrate how these technologies could contribute tobetter preventive strategies.

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Modeling of diagnosis for metabolic syndrome by integrating symptoms into physiochemical indexes

Cited by 23 publications

References 14 publications

Current status and trends of artificial intelligence research on the four traditional Chinese medicine diagnostic methods: a scientometric study

Current status and trends of artificial intelligence research on the four traditional Chinese medicine diagnostic methods: a scientometric study

A Non-Invasive Hydration Monitoring Technique Using Microwave Transmission and Data-Driven Approaches

“Big Data” Approaches for Prevention of the Metabolic Syndrome

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