A Microcosmic Syndrome Differentiation Model for Metabolic Syndrome with Multilabel Learning

Xia, Shujie; Zhang, Jia; Du, Guodong; Li, Shaozi; Vong, Chi Teng; Yang, Zhaoyang; Xin, Jiliang; Zhu, Lei; Gao, Bizhen; Li, Candong

doi:10.1155/2020/9081641

Cited by 15 publications

(11 citation statements)

References 17 publications

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“…Several studies have investigated different data mining methods to identify Chinese medicine syndrome of different diseases such as metabolic syndrome, coronary heart disease, IgA nephropathy, chronic hepatitis B, and acute exacerbation of chronic obstructive pulmonary disease [ 9 , 11 , 12 , 26 , 27 ]. In the present study, the clinical manifestations of 554 cases with T2-DM syndrome are complicated, which is the observation basis of TCM syndrome differentiation treatment.…”

Section: Discussionmentioning

confidence: 99%

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Study of TCM Syndrome Identification Modes for Patients with Type 2 Diabetes Mellitus Based on Data Mining

Tieniu

Yang²,

Wan³

et al. 2021

Evidence-Based Complementary and Alternative Medicine

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Objective. To 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. Methods. A 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 K-nearest neighbor to establish a diagnostic model for effectively distinguishing the typical syndromes in T2-DM patients. Results. The 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 K-nearest neighbor method to identify the two syndromes showed an accurate diagnostic rate of 88.3%. Three major statistically significant predictors included in the model were slippery and replete pulse, polyphagia, and weak pulse ( P < 0.05 ). Conclusion. A 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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Section: Discussionmentioning

confidence: 99%

“…Furthermore, there are growing statistical models and data mining methods that have been used in medical research [8][9][10]. In recent years, a variety of data analysis methods had also been used in the quantitative and objective diagnosis of TCM syndrome [11,12].…”

Section: Introductionmentioning

confidence: 99%

Study of TCM Syndrome Identification Modes for Patients with Type 2 Diabetes Mellitus Based on Data Mining

Tieniu

Yang²,

Wan³

et al. 2021

Evidence-Based Complementary and Alternative Medicine

View full text Add to dashboard Cite

show abstract

“…TCM syndrome element differentiation is a new syndrome differentiation system put forward by professors Wenfeng Zhu and Yongyan Wang [11][12][13], which facilitates quantification of TCM syndrome differentiation [14][15][16]. TCM syndrome elements are the indecomposable diagnostic units that make up the syndrome, with independent connotation, including disease location syndrome elements, such as the heart, liver, or spleen, and disease nature syndrome elements, such as Qi deficiency, blood stasis, or phlegm.…”

Section: Introductionmentioning

confidence: 99%

Relationship between Traditional Chinese Medicine Syndrome Elements and Prognosis of Patients with IgA Nephropathy

Duan

Liu

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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Objective. It is not clear which Traditional Chinese Medicine- (TCM-) related elements affect primary IgA nephropathy (IgAN) progression. Here, we explored the risk factors, based on TCM syndrome elements, related to the prognosis of primary IgAN patients. Methods. We analyzed patients with newly diagnosed, biopsy-proven IgAN at a single institution from December 2013 to September 2021. Basic clinical and pathological characteristics were assessed at the time of renal biopsy. The study endpoint was end-stage renal disease (ESRD: eGFR <15 ml/min per 1.73 m2, dialysis, or kidney transplantation) and/or eGFR decreased by >30% from baseline. Kaplan‒Meier survival analysis was used to explore the role of TCM syndrome elements in IgAN progression. Multivariate Cox regression analysis with adjustment for traditional risk factors was performed to explore TCM syndrome elements that may influence patient prognosis. The factors correlated with TCM syndrome elements in IgAN patients were further evaluated by logistic regression analysis. Results. During a median follow-up of 22.0 months, 53 (12.5%) of the 423 included IgAN patients reached the study endpoint. The main IgAN disease location elements were the kidney, liver, and spleen. The main IgAN disease nature elements were Yin-deficiency and Qi-deficiency, dampness, Yang-deficiency, phlegm, and Blood-deficiency. Kaplan‒Meier analysis identified three disease locations (liver, spleen, and kidney) and four disease natures (Qi-deficiency, Yang-deficiency, phlegm, and dampness) as elements associated with poor renal survival in IgAN patients. In multivariate Cox regression analysis, baseline Yang-deficiency was an independent risk predictor of poor prognosis in primary IgAN patients (hazard ratio 2.338; 95% confidence interval [CI]: 1.208–4.525; P = 0.012 ) after adjustment for traditional risk factors. Furthermore, logistic regression analysis identified being female (odds ratio [OR] 2.518; 95% CI: 1.538–4.122; P < 0.001 ), older age (OR 1.043; 95% CI: 1.022–1.065; P < 0.001 ), low hemoglobin levels (OR 0.984; 95% CI: 0.971–0.996; P = 0.013 ), and cellular/fibrocellular crescents (OR 1.706; 95% CI: 1.068–2.728; P = 0.026 ) as factors affecting Yang-deficiency in IgAN patients. Conclusions. Yang-deficiency independently predicts the risk of poor prognosis in primary IgAN patients. Being female, older age, low hemoglobin levels, and cellular/fibrocellular crescents were independently associated with Yang-deficiency in IgAN patients.

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“…Microsyndrome differentiation is a method of using modern advanced technology to go deep into the body's microcosmic level to understand and differentiate syndromes on the basis of macroscopic syndrome differentiation. Microsyndrome differentiation can be used to guide disease differentiation and syndrome differentiation, explore the cause and pathogenesis, and evaluate the efficacy and guide the prognosis of the disease [ 14 ]. Previous studies have verified that there is a close relationship between different syndromes and physical and chemical indices.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Syndrome Classification of Non‐Small‐Cell Lung Cancer Based on Data of Tongue and Pulse with Machine Learning

Shi

Liu

et al. 2021

BioMed Research International

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Objective. To explore the data characteristics of tongue and pulse of non-small-cell lung cancer with Qi deficiency syndrome and Yin deficiency syndrome, establish syndrome classification model based on data of tongue and pulse by using machine learning methods, and evaluate the feasibility of syndrome classification based on data of tongue and pulse. Methods. We collected tongue and pulse of non-small-cell lung cancer patients with Qi deficiency syndrome ( n = 163 ), patients with Yin deficiency syndrome ( n = 174 ), and healthy controls ( n = 185 ) using intelligent tongue diagnosis analysis instrument and pulse diagnosis analysis instrument, respectively. We described the characteristics and examined the correlation of data of tongue and pulse. Four machine learning methods, namely, random forest, logistic regression, support vector machine, and neural network, were used to establish the classification models based on symptom, tongue and pulse, and symptom and tongue and pulse, respectively. Results. Significant difference indices of tongue diagnosis between Qi deficiency syndrome and Yin deficiency syndrome were TB-a, TB-S, TB-Cr, TC-a, TC-S, TC-Cr, perAll, and the tongue coating texture indices including TC-CON, TC-ASM, TC-MEAN, and TC-ENT. Significant difference indices of pulse diagnosis were t4 and t5. The classification performance of each model based on different datasets was as follows: tongue and pulse < symptom < symptom and tongue and pulse. The neural network model had a better classification performance for symptom and tongue and pulse datasets, with an area under the ROC curves and accuracy rate which were 0.9401 and 0.8806. Conclusions. It was feasible to use tongue data and pulse data as one of the objective diagnostic basis in Qi deficiency syndrome and Yin deficiency syndrome of non-small-cell lung cancer.

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A Microcosmic Syndrome Differentiation Model for Metabolic Syndrome with Multilabel Learning

Cited by 15 publications

References 17 publications

Study of TCM Syndrome Identification Modes for Patients with Type 2 Diabetes Mellitus Based on Data Mining

Study of TCM Syndrome Identification Modes for Patients with Type 2 Diabetes Mellitus Based on Data Mining

Relationship between Traditional Chinese Medicine Syndrome Elements and Prognosis of Patients with IgA Nephropathy

A New Method for Syndrome Classification of Non‐Small‐Cell Lung Cancer Based on Data of Tongue and Pulse with Machine Learning

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