ArticleEvidence-based complementary and alternative medicine : eCAM2021
Study of TCM Syndrome Identification Modes for Patients with Type 2 Diabetes Mellitus Based on Data Mining.
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 3 papers.
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Who cites it
3 citing papers in PubMed, 7 citations in OpenAlex.
- [Relationship Between Different Traditional Chinese Medicine Syndrome Types and Gut Microbiota in Patients With Type 2 Diabetes Mellitus].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2025Article
- Hypoglycemic Effect of an Herbal Decoction (Modified Gangsimtang) in a Patient with Severe Type 2 Diabetes Mellitus Refusing Oral Anti-Diabetic Medication: A Case Report.Medicina (Kaunas, Lithuania) · 2023Article
- A Traditional Chinese Medicine Syndrome Classification Model Based on Cross-Feature Generation by Convolution Neural Network: Model Development and Validation.JMIR medical informatics · 2022Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo establish the diagnosis model for syndromes of type 2 diabetes mellitus (T2-DM) and explore symptoms, the pulse and tongue signs, and laboratory indexes related to syndromes of T2-DM.
methodsA syndromatologic and laboratory investigation was conducted in 554 T2-DM patients with 58 symptoms, 14 tongue signs, 6 pulse signs, and 12 laboratory indexes. The clinical data on the syndrome were collected and analyzed by using logistic regression analysis, decision tree, and
resultsThe most typical syndromes revealed in T2-DM were stomach heat flourishing (SHF) syndrome (261 patients, accounting for 47.1%) and Qi-Yin deficiency (QYD) syndrome (293 patients, 52.9%). According to the clinical data of the patients with these two syndromes, variables including 6 symptoms and signs, 2 pulse signs, 1 tongue sign, and 2 laboratory indicators were introduced into the logistic regression model. All of them were statistically significant. Then, a diagnostic model constructed by QUEST and CHAID algorithms of the decision tree for identifying the two syndromes was proved to have an accurate diagnostic rate of 85.2%. It was found that the following sign and symptoms were effective to differentiate these two syndromes: odor in the mouth, polyphagia, vulnerability to starvation, burning sensation in the stomach, fatigue, limb weakness, slippery and replete pulse, weak pulse, pink tongue, oral glucose tolerance test, and hemoglobin A1C. A classification model constructed by the
conclusionA model for distinguishing the two typical syndromes (SHF syndrome and QYD syndrome) in T2-DM patients was effectively established. This model could help to provide methodological support for the standardization of traditional Chinese medicine (TCM) syndrome differentiation methods.
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Registered trials
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