ArticleHeliyon2023
Predictive value of TCM clinical index for diabetic peripheral neuropathy among the type 2 diabetes mellitus population: A new observation and insight.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Machine Learning-Based Prediction Model for Type 2 Diabetic Peripheral Neuropathy: Role of Bilateral Brachial-Ankle Pulse Wave Velocity and Anthropometric Indicators.International journal of general medicine · 2026Article
- Systematic review and critical appraisal of predictive models for diabetic peripheral neuropathy: Existing challenges and proposed enhancements.World journal of diabetes · 2025Article
- Diabetic peripheral neuropathy detection of type 2 diabetes using machine learning from TCM features: a cross-sectional study.BMC medical informatics and decision making · 2025Observational
- Explainable machine learning model and nomogram for predicting the efficacy of Traditional Chinese Medicine in treating Long COVID: a retrospective study.Frontiers in medicine · 2025Article
- Multi-feature integrated machine learning prediction model for early nephropathy in elderly living with type 2 diabetes mellitus.Frontiers in endocrinology · 2025Article
- The Relationship Between Computerized Face and Tongue Image Segmentation and Metabolic Parameters in Patients with Type 2 Diabetes Based on Machine Learning.Diabetes, metabolic syndrome and obesity : targets and therapy · 2024Article
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Authors and funding
11 authors.
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Abstract
Aims: The objectives of this study were to identify clinical predictors of the Traditional Chinese medicine (TCM) clinical index for diabetic peripheral neuropathy (DPN) in type 2 diabetes mellitus (T2DM) patients, develop a clinical prediction model, and construct a nomogram. Methods: We collected the TCM clinical index from 3590 T2DM recruited at the Second Affiliated Hospital of Tianjin University of Traditional Chinese Medicine from January 2019 to October 2020. The participants were randomly assigned to either the training group (n = 3297) or the validation group (n = 1426). TCM symptoms and tongue characteristics were used to assess the risk of developing DPN in T2DM patients. Through 5-fold cross-validation in the training group, the least absolute shrinkage and selection operator (LASSO) regression analysis method was used to optimize variable selection. In addition, using multifactor logistic regression analysis, a predictive model and nomogram were developed. Results: A total of eight independent predictors were found to be associated with the DPN in multivariate logistic regression analyses: advanced age of grading (odds ratio/OR 1.575), smoke (OR 2.815), insomnia (OR 0.557), sweating (OR 0.535), loose teeth (OR 1.713), dry skin (OR 1.831), purple tongue (OR 2.278). And dark red tongue (OR 0.139). The model was constructed using these eight predictor's medium discriminative capabilities. The area under the curve (AUC) of the training set is 0.727, and the AUC of the validation set is 0.744 on the ROC curve. The calibration plot revealed that the model's goodness-of-fit is satisfactory. Conclusions: We established a TCM prediction model for DPN in patients with T2DM based on the TCM clinical index.
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