ArticleFrontiers in physiology2021
A New Approach of Fatigue Classification Based on Data of Tongue and Pulse With Machine Learning.
Article in Frontiers in physiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Brain-muscle axis regulation of neuroinflammation and sarcopenia in Parkinson's disease: the bridging role of lactylation.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Review
- Traditional Chinese Medicine Modernization in Diagnosis and Treatment: Utilizing Artificial Intelligence and Nanotechnology.MedComm · 2026Review
- Objective pulse wave amplitude as a potential systemic correlate in childhood myopia: a digital TCM-based study.Frontiers in medicine · 2026Article
- An AI-powered tongue image model for home-based monitoring of liver fibrosis.NPJ digital medicine · 2025Article
- Artificial Intelligence in Traditional Chinese Medicine: Multimodal Fusion and Machine Learning for Enhanced Diagnosis and Treatment Efficacy.Current medical science · 2025Review
- The integration of machine learning into traditional Chinese medicine.Journal of pharmaceutical analysis · 2025Review
- Digital intelligence technology: new quality productivity for precision traditional Chinese medicine.Frontiers in pharmacology · 2025Review
- Tongue color parameters in predicting the degree of coronary stenosis: a retrospective cohort study of 282 patients with coronary angiography.Frontiers in cardiovascular medicine · 2024Article
- Predicting the diabetic foot in the population of type 2 diabetes mellitus from tongue images and clinical information using multi-modal deep learning.Frontiers in physiology · 2024Article
- Machine learning prediction models for different stages of non-small cell lung cancer based on tongue and tumor marker: a pilot study.BMC medical informatics and decision making · 2023Article
- Machine learning in TCM with natural products and molecules: current status and future perspectives.Chinese medicine · 2023Review
- Current status and trends of artificial intelligence research on the four traditional Chinese medicine diagnostic methods: a scientometric study.Annals of translational medicine · 2023Article
- A lung cancer risk warning model based on tongue images.Frontiers in physiology · 2023Article
- Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals.Frontiers in physiology · 2022Article
- A new method for assessing chronic insomnia: Machine learning-based fusion of TCM observation digital features.Digital healthArticle
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12 authors.
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Abstract
backgroundFatigue is a common and subjective symptom, which is associated with many diseases and suboptimal health status. A reliable and evidence-based approach is lacking to distinguish disease fatigue and non-disease fatigue. This study aimed to establish a method for early differential diagnosis of fatigue, which can be used to distinguish disease fatigue from non-disease fatigue, and to investigate the feasibility of characterizing fatigue states in a view of tongue and pulse data analysis.
methodsTongue and Face Diagnosis Analysis-1 (TFDA-1) instrument and Pulse Diagnosis Analysis-1 (PDA-1) instrument were used to collect tongue and pulse data. Four machine learning models were used to perform classification experiments of disease fatigue vs. non-disease fatigue.
resultsThe results showed that all the four classifiers over "Tongue & Pulse" joint data showed better performances than those only over tongue data or only over pulse data. The model accuracy rates based on logistic regression, support vector machine, random forest, and neural network were (85.51 ± 1.87)%, (83.78 ± 4.39)%, (83.27 ± 3.48)% and (85.82 ± 3.01)%, and with Area Under Curve estimates of 0.9160 ± 0.0136, 0.9106 ± 0.0365, 0.8959 ± 0.0254 and 0.9239 ± 0.0174, respectively.
conclusionThis study proposed and validated an innovative, non-invasive differential diagnosis approach. Results suggest that it is feasible to characterize disease fatigue and non-disease fatigue by using objective tongue data and pulse data.
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