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Objective: To study the effects of Xiaoyao Powder (XYP) and its composition drug groups (Shugan group and Jianpi group) on the behavior medicinal effects of depressed rats. In addition, the rough set theory and the model of decision rules were used to analyze the interaction between composition drug groups and their effects on the anti-depression of XYP. This study will provide the research method and theoretical basis for the scientific explanation of the compatibility rule of anti-depression of XYP. Methods: The depression rats model was constructed by Chronic Unpredictable Mild Stress (CUMS). The effects of XYP and its composition drug groups on CUMS rats were investigated in open field test, sucrose preference rate and forced swimming. Afterwards, Rosetta software was used to construct the decision rules model of rough set. The interactions among XYP, Shugan, and Jianpi groups were analyzed according to the decision rules. Results: XYP and its composition drug groups significantly improved the depressive behaviors of CUMS rats. Compared with the model group, XYP and its composition drug groups significantly increased the number of crossings and rearing of the rats in the open field test (P Jianpi group > Shugan group. The order of improving the behaviors in the open field test was Shugan group > Jianpi group > XYP. The order of decreasing the time of forced swimming index was XYP = Shugan group > Jianpi group. The results of decision rules showed that, Shugan and Jianpi groups exhibited certain antidepressant effects when used alone, but they were weaker than that of XYP. Of note, Shugan and Jianpi groups exhibited synergetic effect in a dose-dependent manner: The higher the dose was, the greater the antidepressant effect was. In order to further verify the mutual relationship between Shugan and Jianpi groups on the anti-depression efficacy, according to the decision table of XYP and its individual herbs, attribute reduction of eight herbs in XYP was conducted, obtaining the representative drugs of Shugan and Jianpi groups: Bupleuri Radix and Paeoniae Radix Alba. The results of decision rules were consistent with that of composition drug groups. Conclusions XYP and its composition drug groups can not only improve the core symptoms of depression, including index in open field test, sucrose preference rate and forced swimming index, showing remarkable antidepressant effects. The effects of XYP on these indexes were stronger than that of its composition drug groups, indicating that the composition of the XYP prescription is well-suited and scientifically reasonable. The decision rules based on rough set theory could be used as an approach to scientifically interpret the compatibility laws of TCM prescriptions.
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Idiosyncratic hepatotoxicity of Polygonum multiflorum has attracted a great attention in the world. The most toxic part of idiosyncratic hepatotoxicity was screened by MTT assay and flow cytometry, which was the 50% ethanol elute by macroporous adsorptive resins from alcohol-extraction of P. multiflorum. The fingerprints were collected by HPLC from 50% ethanol elute of crude and processed P. multiflorum from different habitats, then 14 common peaks were determined. Spectrum-toxicity relationship was analyzed by rough set theory(RST). Two main chemical components were predicted for idiosyncratic hepatotoxicity, in which TSG was the greater contributor. Idiosyncratic hepatotoxicity of TSG was tested in vitro, and the results indicated that TSG was the most important constituent contributed to idiosyncratic hepatotoxicity of P. multiflorum. The study showed the discovery of the main chemical components for idiosyncratic hepatotoxicity, and RST was effective for analyzing the spectrum-toxicity relationship, which could be a new method used in the effective/toxic constituents field of traditional Chinese medicine.
Subject(s)
Humans , Chemical and Drug Induced Liver Injury , Chromatography, High Pressure Liquid , Drugs, Chinese Herbal , Fallopia multiflora , Chemistry , Medicine, Chinese Traditional , PhytochemicalsABSTRACT
Objective To survey the correlation among ADR performances of Shuxuening injection(SXN injection ) based on ADR reports and literature data by rough set theory(RST). Methods By collecting 468 ADR reports of SXN injection,data attribute reduction were processed by Rossata software,and the generated rules and each indicator were analyzed. Results 9 main indicators,which mainly belongs to medication information,had a strong relationship with ADR.There was a correlation among ADR performances.Relationship could also found between performances and medication information. Conclusion The results of the study provide reference for clinical reasonable application of SXN injection,and enrich the research method of ADR.
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Objective To establish an evaluation index system of electricity saving at general hospitals.Methods Based on civil building energy saving studies and in accordance with national regulations on hospital energy saving, the authors build an electricity saving index system for general hospitals.The indexes were reduced by the rough set theory, and their weight was determined by analytical hierarchy process and expert analysis.Results An electricity saving evaluation index system for general hospitals is so developed, consisting of six level-2 indexes and 27 level-3 indexes.Conclusions Such an evaluation index system can guide hospital electricity consumption and saving.
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Objective: Develop an assistant diagnosis system for arrhythinias which can reduce doctors' workload and improve their veracity of diagnosing ECG. Methods: Firstly, this paper achieves filter and detection of waves using wavelet transform and extracts parameters; Secondly, it reduces parameters using rough set and identifies arrhythmias according to relevant rules; At last, it gets the membership of abnormal heart beat by fuzzy neural network. Results: The main modules such as filter, detection of waves and identification of arrhythmias are achieved well, and a complete system is formed. Conclusions: This system can identify nineteen arrhythmaias and get their membership and position. It could assist doctors in making the proper diagnosis on ECG potentially.
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@# Objective To study the feasibility of using rough set and support vector machines to detect Parkinson disease. Methods The reduction algorithm based on the importance of the attributes in the rough set theory was used to reduce the common diagnosis features in the clinical practice. The support vector machines were applied for classification with the linear, polynomial and RBF kernel, and the Results were compare with that of BP neural network. Results The algorism combined attributes reduction and support vector machines appeared the highest accuracy of 92.71% in the classification, which seemed greater advantage in accuracy and stability than BP neural network. Conclusion Improving the accuracy of the classification as well as saving the resources, rough set and support vector machines are proved to be an effective method to assist the clinical diagnosis of Parkinson disease.
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In order to select effective feature subsets for pattern classification, a novel statistics rough set method is presented based on generalized attribute reduction. Unlike classical reduction approaches, the objects in universe of discourse are signs of training sample sets and values of attributes are taken as statistical parameters. The binary relation and discernibility matrix for the reduction are induced by distance function. Furthermore, based on the monotony of the distance function defined by Mahalanobis distance, the effective feature subsets are obtained as generalized attribute reducts. Experiment result shows that the classification performance can be improved by using the selected feature subsets.
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In order to select effective feature subsets for pattern classification, a novel statistics rough set method is presented based on generalized attribute reduction. Unlike classical reduction approaches, the objects in universe of discourse are signs of training sample sets and values of attributes are taken as statistical parameters. The binary relation and discernibility matrix for the reduction are induced by distance function. Furthermore, based on the monotony of the distance function defined by Mahalanobis distance, the effective feature subsets are obtained as generalized attribute reducts. Experiment result shows that the classification performance can be improved by using the selected feature subsets.
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Objective Due to the incompleteness and complexity of fault diagnosis for power transformers, a comprehensive rough-fuzzy scheme for solving fault diagnosis problems is presented. Fuzzy set theory is used both for representation of incipient faults' indications and producing a fuzzy granulation of the feature space. Rough set theory is used to obtain dependency rules that model indicative regions in the granulated feature space. The fuzzy membership functions corresponding to the indicative regions, modelled by rules, are stored as cases. Results Diagnostic conclusions are made using a similarity measure based on these membership functions. Each case involves only a reduced number of relevant features making this scheme suitable for fault diagnosis. Conclusion Superiority of this method in terms of classification accuracy and case generation is demonstrated.
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KDD has an abroad application foreground in hospital,espectially in HIS,clinical asistance,detection,and diagnosis.Some research achievements has been applied in hospital diagnosing and treatment.Such aspects of KDD are discussed as manual neural network,fuzzy system evolutionary algorithms,rough set,decision tree,support vector machine,etc.[Chinese Medical Equipment Journal,2008,29(2):74-75]
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Rough set is a mathematical approach to study imprecise, uncertain or incomplete information or knowledge. The data mining technology based on the rough set theory can be trained through the training data set. The technology is applied to such fields as the prognosis of the pain in the chest, emergence diagnosis in ICU, distinguishing and diagnosis of lung tumor and rheumatoid disease, diagnosis of galactophore disease, diagnosis of premature birth, classification and diagnosis of liver disease and acute appendicitis. The technology has a brilliant future in medical data mining.