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Supplemental material, sj-pdf-1-dhj-10.1177_20552076231160323 for A new model for predicting the occurrence of polycystic ovary syndrome: Based on data of tongue and pulse by Weiying Wang, Weiwei Zeng, Shunli He, Yulin Shi, Xinmin Chen, Liping Tu, Bingyi Yang, Jiatuo Xu and Xiuqi Yin in Digital Health

Background: Carrying out routine diabetes risk assessment and prediction is conducive to controlling the incidence of diabetes. Previous studies have confirmed that tongue features can reflect changes in glucose metabolism. We want to find the key tongue features related to glucose metabolism for early warning of prediabetes and diabetes. This will benefit the prevention and control of diabetes.Methods: We investigated 719 subjects in Shuguang Hospital affiliated with Shanghai University of TCM in Shanghai, China, during the August 2018 – December 2019 period. We used PCA to reconstruct 25 new features on the basis of the original features, and used logistic regression to analyze these new features. Based on the factor loading method, we associate the new features with the original features. Finally, we determined the key tongue features and change patterns related to prediabetes risk and diabetes risk.Results: For the prediabetes, TB-ASM, TC-ASM and TB-Cr are protective factors. TB-CON, TC-CON, TB-MEAN, TC-MEAN, TB-ENT and TC-ENT are risk factors. For the diabetes, TB-a, TB-S, TB-Cr, TB-b and TC-b are protective factors, and Per-all and TB-Cb are risk factors. Conclusion: The PCA eliminates the redundancy of original features and refines the original features. The PCA combined with logistic regression found the key tongue features reflecting glucose metabolism, and clarified the significance of the changes in these tongue features in the prediabetes and diabetes population. This study provides a research foundation for using tongue features to implement early warning of diabetes risk.

Background: Lung cancer is a common malignant tumor that affects people's health seriously. Traditional Chinese medicine (TCM) is one of the effective methods for the treatment of advanced lung cancer, accurate TCM syndrome differentiation is essential to treatment. When the symptoms are not obvious, the traditional symptom-based syndrome differentiation cannot be carried out. There is a close relationship between syndrom and index of western medicine, the combination of micro index and macro symptom can assist syndrome differentiation effectively.Methods: Tongue and pulse data of non-small cell lung cancer (NSCLC) patients with Qi deficiency syndrome (n=163), patients with Yin deficiency syndrome (n=174) and healthy controls (n=185) were collected by using intelligent Tongue and Face Diagnosis Analysis-1 instrument and Pulse Diagnosis Analysis-1 instrument, respectively. The characteristics of tongue and pulse data were analyzed, the correlation analysis was also made on tongue and pulse data. And four machine learning methods, namely Random Forest, Logistic Regression, Support Vector Machine and Neural Network, were used to establish the classification models based on symptoms, tongue & pulse data, and symptoms & tongue & pulse data, respectively.Results: Significant difference indexes 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 indexes including TC-Con, TC-ASM, TC-MEAN, and TC-ENT. Significant difference indexes of pulse diagnosis were t4 and t5. The classification performance of each model based on different data sets was as follows: model of tongue & pulse data Conclusions:This study explored the characteristics of tongue and pulse data of NSCLC with Qi deficiency syndrome and Yin deficiency syndrome, and established syndromes classification model. It was feasible to use tongue and pulse data as one of the objective diagnostic indexes in Qi deficiency syndrome and Yin deficiency syndrome of NSCLC.Name of the registry: Chinese Clinical Trial RegistryTrial registration number: ChiCTR1900026008; ChiCTR-IOR-15006502 Date of registration: Jun. 04th, 2015URL of trial registry record: http://www.chictr.org.cn/showprojen.aspx?proj=11119;http://www.chictr.org.cn/edit.aspx?pid=38828&htm=4 (This is a retrospective registration)

BACKGROUND Fatigue is one of the most common subjective symptom,due to lack of objective diagnostic criteria ,it is often neglected,especially in the early stage of disease.It is very important to establish an objective evaluation method for diagnosis of disease fatigue and non-disease fatigue based on tongue and pulse data. Our research team has been engaged in the informatization and intelligentization research of TCM diagnosis technology for more than 30 years, we have been engaged in health evaluation and analysis of large clinical sample data for many years, and accumulated a certain amount of typical data,in this area of research, foundation of our work has outstanding advantages. OBJECTIVE The purposes of this study is to explore a new method for clinical diagnosis of fatigue, by analyzing the characteristic of objective tongue and pulse data of disease fatigue subjects and sub-health fatigue subjects, and establishing the diagnosis and classification models of sub-health fatigue and disease fatigue based on the tongue and pulse data. METHODS In this study,736 subjects were selected from the Physical Examination Center, and were divided into healthy control, sub-health fatigue group and disease fatigue group according to physical examination indexes and health status assessment criteria. TFDA-1 tongue diagnosis instrument and PDA-1 pulse diagnosis instrument independently developed were used to collect the tongue image and sphygmogram of the subjects. Multivariate statistical analysis methods were usded to analyze the tongue and pulse data distribution pattern of the fatigue subjects, and machine learning methods were used to establish the diagnostic classification models of fatigue. RESULTS The tongue feature TB-a,TB-b,TB-H,TB-S,TB-I,TB-Cb,TC-L,TC-H,TC-I,TC-Y,TC-Cr,TC-Cb,perAll and perPart were statistically significant different among three groups (P < 0.05, P < 0.01). The sphygmogram feature t1,t4,h5,W1,W2,W1/t and W2/t showed statistically significant differences among three groups (P < 0.05, P < 0.01). There was a canonical correlation between the tongue and pulse data in the disease fatigue group, and the correlation coefficient was 0.649 (P CONCLUSIONS Sub-health fatigue and disease fatigue subjects had different tongue and pulse characteristic. Diagnosis and classification models can be established using basic information, tongue and pulse data to explore a new method for evaluating fatigue state.