ArticleEvidence-based complementary and alternative medicine : eCAM2021
Application of Artificial Intelligence in the Establishment of an Association Model between Metabolic Syndrome, TCM Constitution, and the Guidance of Medicated Diet Care.
Article in Evidence-based complementary and alternative medicine : eCAM, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Association between Traditional Chinese Medicine constitution and geriatric functional impairment evaluated by the integrated care for older people approach in the elderly.Journal of traditional and complementary medicine · 2026Article
- Advancing the modernization of traditional Chinese medicine through artificial intelligence and multimodal data integration.Chinese medicine · 2026Review
- Exploring the application of machine learning to identify the correlations between phthalate esters and disease: enhancing nursing assessments.Health information science and systems · 2025Article
- Dietary Habits, TCM Constitutions, and Obesity: Investigating the Protective Effects of Vegetarian Dietary Patterns in Taiwan.Healthcare (Basel, Switzerland) · 2025Article
- Construction of machine learning-based models for screening the high-risk patients with gastric precancerous lesions.Chinese medicine · 2025Article
- Digital intelligence technology: new quality productivity for precision traditional Chinese medicine.Frontiers in pharmacology · 2025Review
- Impact of sleep duration and dietary patterns on risk of metabolic syndrome in middle-aged and elderly adults: a cross-sectional study from a survey in Anhui, Eastern China.Lipids in health and disease · 2024Article
- Metabolic syndrome prediction model using Bayesian optimization and XGBoost based on traditional Chinese medicine features.Heliyon · 2023Article
- Comparison of the predictive value of four insulin resistance surrogates for the prevalence of hypertension: a population-based study.Diabetology & metabolic syndrome · 2022Article
- Application of Machine Learning in Developing Decision-Making Support Models for Decompressed Vertebroplasty.Healthcare (Basel, Switzerland) · 2022Article
- Association of Lipid Levels With the Prevalence of Hypertension in Chinese Women: A Cross-Sectional Study Based on 32 Health Check Centers.Frontiers in endocrinology · 2022Article
- Relationship Between Lipid Profiles and Hypertension: A Cross-Sectional Study of 62,957 Chinese Adult Males.Frontiers in public health · 2022Article
- Differences in Lipid Profiles and Atherogenic Indices Between Hypertensive and Normotensive Populations: A Cross-Sectional Study of 11 Chinese Cities.Frontiers in cardiovascular medicine · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study conducted exploratory research using artificial intelligence methods. The main purpose of this study is to establish an association model between metabolic syndrome and the TCM (traditional Chinese medicine) constitution using the characteristics of individual physical examination data and to provide guidance for medicated diet care.
methodsBasic demographic and laboratory data were collected from a regional hospital health examination database in northern Taiwan, and artificial intelligence algorithms, such as logistic regression, Bayesian network, and decision tree, were used to analyze and construct the association model between metabolic syndrome and the TCM constitution.
conclusionIn order to increase the public's knowledge and methods for mitigating metabolic syndrome, in the future, nursing staff can provide nonprescription medicated diet-related nursing guidance information via the prediction and assessment of the TCM constitution.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.