Hyperlipidemia is one of the main basic lesions of blood vessels and organ diseases. Traditional Chinese medicine (TCM) has been used in the prevention and treatment of hyperlipidemia with advantages such as little drug side effects and obvious efficacy. The establishment of hyperlipidemia diagnostic aid platform helped to improve treatment efficiency, and provide a platform for data acquisition, storage and analysis for research on hyperlipidemia syndrome standardization. At first, a collection system was implemented, which was consisted of tongue image and the text information of the other three diagnostic methods, for doctor's accurate mastering of patient's clinical manifesta-tions and improvement of therapeutic effect. Through this platform, a clustering algorithm was applied to analyze clin-ical information of four diagnostic methods among 897 hyperlipemia cases, which had been classified into 5 cate-gories. Compare disease characteristics of samples in every category with syndromes category and find out the rela-tionships between them, by which the foundation of hyperlipemia syndrome differentiation standardization will be es-tablish. It provided suggestions on TCM syndrome differentiation and diagnosis.

This study was aimed to establish fingerprints of Citri Sarcodactylis Fructus from different geographical origins,and to use the method of pattern recognition to compare the differences of Citri Sarcodactylis Fructus from different habitats.In this study,high performance liquid chromatography (HPLC) was used to establish fingerprints for 25 batches of Citri Sarcodactylis Fructus from 4 habitats.Furthermore,similarity evaluation,cluster analysis (CA) and principal component analysis (PCA) were performed.The results from established fingerprints showed that a total of 26 common peaks were pointed out and 4 peaks were identified as the common peaks.The CA and PCA can be used to compare Citri Sarcodactylis Fructus from different habitats.It was concluded that Cirri Sarcodactylis Fructus in near geographic origins had a higher similarity,while the different geographic origins had a higher difference.

ObjectiveTo compare the diagnostic accuracy of five different weighting methods of Chinese medicine syndrome and then analyze their diagnostic efficacy and characteristics， by taking Diagnostic Standard for Type 2 Diabetes Mellitus （T2DM） with Dampeness-heat Syndrome （abbreviated as diagnostic standard） as an example. MethodsData from expert questionnaire on the diagnostic standard and a cross-sectional survey of 1021 patients were collected. The comparative diagnostic test accuracy （CDTA） method was used to calculate the area under the ROC curve （AUC）， area under the PR curve （AUPR）， accuracy （ACC）， sensitivity， and specificity of five commonly used weighting methods in two categories， including knowledge-driven weighting methods （expert scoring synthesis method， analytic hierarchy process， and precedence chart method） and data-driven weighting methods （logistic regression contribution method and entropy weighting method）. ResultsAmong 1021 patients with T2DM， 389 cases were diagnosed as dampness-heat syndrome. The expert scoring synthesis method， analytic hierarchy process method， and precedence chart method were basically consistent in the weight scores of each item. The expert scoring comprehensive method， analytic hierarchy process method， and entropy weighting method have a smaller difference in the weight scores of each item， while there was larger difference in the weight scores of each item of the precedence chart method and the logistic regression contribution method. The AUC （95% CI）， AUPR， ACC， sensitivity， and specifi-city of the expert scoring synthesis method were 0.913 （0.893， 0.932）， 0.851， 0.870， 0.868 and 0.875， respectively； while those of the analytic hierarchy process method were 0.910 （0.890， 0.930）， 0.838， 0.879， 0.848 and 0.896； of the precedence chart method were 0.919 （0.900， 0.937）， 0.858， 0.875， 0.871 and 0.875； of the logistic regression contribution method were 0.867 （0.842， 0.891）， 0.792， 0.853， 0.769 and 0.898； and of the entropy weighting method were 0.895 （0.873， 0.916）， 0.820， 0.869， 0.802 and 0.908. ConclusionThe knowledge-driven weighting methods are better than the data-driven weighting methods in terms of diagnostic efficacy and reflecting expert experience.