Study on the medication rules of traditional Chinese medicine in the treatment of sleep disorder after stroke based on data mining

Objective:To explore the medication rule of Traditional Chinese Medicine(TCM)in the treatment of sleep disorder after stroke by using data mining technology.Methods:A computer search was used to search the electronic database of clinical literature on the treatment of sleep disorders after stroke by TCM from January 2000 to January 2021.Excel was used to establish the database,and the prescription information was described and analyzed statistically.Using IBM SPSS Modeler 18.0 software,Apriori algorithm was used for TCM association analysis,and IBM SPSS 22.0 software was used for systematic cluster analysis of high-frequency TCM.Results:A total of 67 literatures were included,covering 131 traditional Chinese medicines.The medecines with a higher frequency of sodium use include Ziziphi Spinosae Semen(Suanzaoren),Angelicae Sinensis Radix(Danggui),Ligusticum(Chuanxiong),liquorice(Gancao),Poria cocos(Fuling),and so on.From the effect point of view,deficiency-tonifying medicine,sedative medicine and blood-activating and stasis-removing medicine are commonly used.The medicinal properties are mainly cold,mild and warm.The main medicine flavor are sweet and bitter.The medicines mostly belong to the liver,heart and spleen Meridian.Thirty-three association rules were obtained for medicine pairs and medicine groups from the correlation analysis,and the core combinations were"Ziziphi Spinosae Semen(Suanzaoren)-Tuber fleeceflower stem(Yejiaoteng)","Ziziphi Spinosae Semen(Suanzaoren)-Polygala(Yuanzhi)","Ziziphi Spinosae Semen(Suanzaoren)-Cortex albiziae(Hehuanpi)"and"Angelicae Sinensis Radix(Danggui)-Radix bupleuri(Chaihu)-Radix Paeoniae Alba(Baishao)"and so on.Seven medicine aggregation groups were obtained by medicine cluster analysis.Conclusion:In the treatment of sleep disorder after stroke by TCM,the main method is to calm the heart and mind.Meanwhile,according to different syndrome types,the treatment methods of tonifying the heart and spleen,nourishing the liver and kidney,soothing the liver and softening the liver,clearing heat and resolving phlegm,nourishing the blood and promoting blood circulation are selected,which provide certain reference for clinical treatment.

An Analysis of Medication Rules of Chinese Medicine for Restless Legs Syndrome Based on Data Mining

Objective: Based on data mining, to explore the medication rules of Chinese medicine for the treatment of restless legs syndrome(RLS). Methods: The CNKI, WANFANG, and VIP were taken as data sources, and "restless legs syndrome, RLS" as the key words, and "Chinese medicine, Chinese materia medica, traditional Chinese medicine(TCM), traditional Chinese and Western medicine" as sub key words, the data was extracted from the journals and literature related to the treatment of RLS by TCM from the establishment of the database to 2020, and data mining techniques(frequency analysis, cluster analysis, association rules) were used to analyze the core drugs and drug pair(group) rules. Results: A total of 87 prescriptions met the requirements of this study, involving 142 Chinese herbal medicines. The top 5 Chinese herbal medicines with a higher frequency of use were Radix Paeoniae Alba, Radix Glycyrrhizae, Radix Angelicae Sinensis, Fructus Chaenomelis and Radix Astragali seu Hedysari. The four Qi(气) of the medicine were mainly warm and neutral, the five flavors were mainly sweet, bitter, and pungent. The main channels of the meridian were mainly the liver meridian, spleen meridian and heart meridian. The medication categories were mainly tonifying deficiency herbs, blood activating and removing blood stasis herbs, and eliminating wind and dampness herbs. The association rule analysis yielded 24 Chinese medicine combinations with high support, and the hierarchical cluster analysis yielded a total of 5 clusters. Conclusion: TCM treatment of RLS is based on tonifying deficiency herbs, especially to replenish Qi and blood throughout the course of the disease, supplemented by herbs for promoting blood circulation and removing blood stasis, and herbs for eliminating wind and dampness, as well as combined with herbs for reliving superficies and herbs for calming the liver to stop the wind.

Data Mining-Based Maintenance Management Framework of Multi-component System

Complex repairable system is composed of thousands of components.Some maintenance management and decision problems in maintenance management and decision need to classify a set of components into several classes based on data mining.Furthermore,with the complexity of industrial equipment increasing,the managers should pay more attention to the key components and carry out the lean management is very important.Therefore,the idea"customer segmentation"of"precise marketing"can be used in the maintenance management of the multi-component system.Following the idea of segmentation,the components of multicomponent systems should be subdivied into groups based on specific attributes relevant to maintenance,such as maintenance cost,mean time between failures,and failure frequency.For the target specific groups of parts,the optimal maintenance policy,health assessment and maintenance scheduling can be determined.The proposed analysis framework will be given out.In order to illustrate the effectiveness of this method,a numerical example is given out.

Hydraulic metal structure health diagnosis based on data mining technology

In conjunction with association rules for data mining, the connections between testing indices and strong and weak association rules were determined, and new derivative rules were obtained by further reasoning. Association rules were used to analyze correlation and check consistency between indices. This study shows that the judgment obtained by weak association rules or non-association rules is more accurate and more credible than that obtained by strong association rules. When the testing grades of two indices in the weak association rules are inconsistent, the testing grades of indices are more likely to be erroneous, and the mistakes are often caused by human factors. Clustering data mining technology was used to analyze the reliability of a diagnosis, or to perform health diagnosis directly. Analysis showed that the clustering results are related to the indices selected, and that if the indices selected are more significant, the characteristics of clustering results are also more significant, and the analysis or diagnosis is more credible. The indices and diagnosis analysis function produced by this study provide a necessary theoretical foundation and new ideas for the development of hydraulic metal structure health diagnosis technology.

Mining Software Repository for Cleaning Bugs Using Data Mining Technique

Despite advances in technological complexity and efforts,software repository maintenance requires reusing the data to reduce the effort and complexity.However,increasing ambiguity,irrelevance,and bugs while extracting similar data during software development generate a large amount of data from those data that reside in repositories.Thus,there is a need for a repository mining technique for relevant and bug-free data prediction.This paper proposes a fault prediction approach using a data-mining technique to find good predictors for high-quality software.To predict errors in mining data,the Apriori algorithm was used to discover association rules by fixing confidence at more than 40%and support at least 30%.The pruning strategy was adopted based on evaluation measures.Next,the rules were extracted from three projects of different domains;the extracted rules were then combined to obtain the most popular rules based on the evaluation measure values.To evaluate the proposed approach,we conducted an experimental study to compare the proposed rules with existing ones using four different industrial projects.The evaluation showed that the results of our proposal are promising.Practitioners and developers can utilize these rules for defect prediction during early software development.

Traditional Chinese medicine Master XIONG Jibo’s medication experience in treating arthralgia syndrome through data mining

Objective This study aimed to examine and propagate the medication experience and group formula of traditional Chinese medicine(TCM)Master XIONG Jibo in diagnosing and treat-ing arthralgia syndrome(AS)through data mining.Methods Data of outpatient cases of Professor XIONG Jibo were collected from January 1,2014 to December 31,2018,along with cases recorded in A Real Famous Traditional Chinese Medicine Doctor:XIONG Jibo's Clinical Medical Record 1,which was published in December 2019.The five variables collected from the patients’data were TCM diagnostic information,TCM and western medicine diagnoses,syndrome,treatment,and prescription.A database was established for the collected data with Excel.Using the Python environment,a custom-ized modified natural language processing(NLP)model for the diagnosis and treatment of AS by Professor XIONG Jibo was established to preprocess the data and to analyze the word cloud.Frequency analysis,association rule analysis,cluster analysis,and visual analysis of AS cases were performed based on the Traditional Chinese Medicine Inheritance Computing Platform(V3.0)and RStudio(V4.0.3).Results A total of 610 medical records of Professor XIONG Jibo were collected from the case database.A total of 103 medical records were included after data screening criteria,which comprised 187 times(45 kinds)of prescriptions and 1506 times(125 kinds)of Chinese herbs.The main related meridians were the liver,spleen,and kidney meridians.The properties of Chinese herbs used most were mainly warm,flat,and cold,while the flavors of herbs were mainly bitter,pungent,and sweet.The main patterns of AS included the damp heat,phlegm stasis,and neck arthralgia.The most commonly used herbs for AS were Chuanniuxi(Cyathu-lae Radix),Huangbo(Phellodendri Chinensis Cortex),Cangzhu(Atractylodis Rhizoma),Qinjiao(Gentianae Macrophyllae Radix),Gancao(Glycyrrhizae Radix et Rhizoma),Huangqi(Astragali Radix),and Chuanxiong(Chuanxiong Rhizoma).The most common effect of the herbs was“promoting blood circulation and removing blood stasis”,followed by“supple-menting deficiency(Qi supplementing,blood supplementing,and Yang supplementing)”,and“dispelling wind and dampness”.The data were analyzed with the support≥15%and con-fidence=100%,and after de-duplication,five second-order association rules,39 third-order association rules,39 fourth-order association rules,and two fifth-order association rules were identified.The top-ranking association rules of each were“Cangzhu(Atractylodis Rhizoma)→Huangbo(Phellodendri Chinensis Cortex)”“Cangzhu(Atractylodis Rhizoma)+Chuanniuxi(Cyathulae Radix)→Huangbo(Phellodendri Chinensis Cortex)”“Chuanniuxi(Cyathulae Radix)+Danggui(Angelicae Sinensis Radix)+Gancao(Glycyrrhizae Radix et Rhizoma)→Qinjiao(Gentianae Macrophyllae Radix)”and“Chuanniuxi(Cyathulae Radix)+Danggui(Angelicae Sinensis Radix)+Gancao(Glycyrrhizae Radix et Rhizoma)+Huangbo(Phello-dendri Chinensis Cortex)→Qinjiao(Gentianae Macrophyllae Radix)”,respectively.Five clusters were obtained using cluster analysis of the top 30 herbs.The herbs were mainly dry-ing dampness,supplementing Qi,and promoting blood circulation.The main prescriptions of AS were Ermiao San(二妙散),Gegen Jianghuang San(葛根姜黄散),and Huangqi Chongteng Yin(黄芪虫藤饮).The herbs of core prescription included Cangzhu(Atractylodis Rhizoma),Chuanniuxi(Cyathulae Radix),Gancao(Glycyrrhizae Radix et Rhizoma),Huangbo(Phellodendri Chinensis Cortex),Mugua(Chaenomelis Fructus),Qinjiao(Gentianae Macro-phyllae Radix),Danggui(Angelicae Sinensis Radix),and Yiyiren(Coicis Semen).Conclusion Clearing heat and dampness,relieving collaterals and pain,and invigorating Qi and blood are the most commonly used therapies for the treatment of AS by Professor XIONG Jibo.Additionally,customized NLP model could improve the efficiency of data mining in TCM.

Data Mining Based on Computational Intelligence

This paper combines computational intelligence tools: neural network, fuzzylogic, and genetic algorithm to develop a data mining architecture (NFGDM), which discovers patternsand represents them in understandable forms. In the NFGDM, input data arepreprocesscd byfuzzification, the preprocessed data of input variables arc then used to train a radial basisprobabilistic neural network to classify the dataset according to the classes considered, A ruleextraction technique is then applied in order to extract explicit knowledge from the trained neuralnetworks and represent it m the form of fuzzy if-then rules. In the final stage, genetic algorithmis used as a rule-pruning module to eliminate those weak rules that are still in the rule bases.Comparison with some known neural network classifier, the architecture has fast learning speed, andit is characterized by the incorporation of the possibility information into the consequents ofclassification rules in human understandable forms. The experiments show that the NFGDM is moreefficient and more robust than traditional decision tree method.

Algorithm of Intrusion Detection Based on Data Mining and Its Implementation

Intrusion detection is regarded as classification in data mining field. However instead of directly mining the classification rules, class association rules, which are then used to construct a classifier, are mined from audit logs. Some attributes in audit logs are important for detecting intrusion but their values are distributed skewedly. A relative support concept is proposed to deal with such situation. To mine class association rules effectively, an algorithms based on FP-tree is exploited. Experiment result proves that this method has better performance.

Data Mining for Quality Prediction in Textile Engineering

A data mining method for quality prediction using association rule (DMAR) is presented in this paper. Association rule is used to mine the valuable relations of items among amounts of textile process data for ANN prediction model. DMAR consists of three main steps: setup knowledge data set; data cleaning and converting; find the item set with large supports and generate the expected rules. DMAR effectively improves the precision of prediction in yarn breaking. It rapidly gets rid of the negative influence of training parameters on prediction model. Then more satisfactory quality prediction result can be reached.

Research on Employment Data Mining for Higher Vocational Graduates

In order to make effective use a large amount of graduate data in colleges and universities that accumulate by teaching management of work, the paper study the data mining for higher vocational graduates database using the data mining technology. Using a variety of data preprocessing methods for the original data, and the paper put forward to mining algorithm based on commonly association rule Apriori algorithm, then according to the actual needs of the design and implementation of association rule mining system, has been beneficial to the employment guidance of college teaching management decision and graduates of the mining results.

A study on the rule of Chinese medicine use for airway remodeling based on data mining

Objective:Use data mining techniques to explore the rule of Chinese medicine used for airway remodeling.Methods:Search the literature on Chinese medicine use for airway remodeling in the past 20 years.With the help of WPS Office Excel 11.1,IBM SPSS Statistics 23.0 and SPSS Modeler 18.0 software,prescriptions were analyzed for the frequency of drug use,the four natures,the five flavours and the channel tropism,cluster analysis and association analysis of high-frequency drugs.Results:There were 58 Chinese medicine prescriptions for airway remodeling be found,involving 105 Chinese medicines,the most frequent channel tropism were spleen,stomach,lung,large intestine,liver and gallbladder,the most frequent use of the five flavors was sour,sweet and pungent,the highest frequency of the four natures was cold and hot,cluster analysis yielded eight drug aggregation groups,and association rule analysis yielded five groups of high-frequency drug pairs.Conclusion:The main TCM treatments for airway remodeling are expelling phlegm,relieving cough,asthma calming,expelling blood stasis and deficiency tonifying.The results of this study can provide ideas for compounding and drug selection for subsequent studies.

Data mining-based analysis of acupoint selection patterns for chronic hepatitis B infection

Background:The purpose of this study was to identify the characteristics and principles of acupoints applied for treating chronic hepatitis B infection.Methods:The published clinical studies on acupuncture for the treatment of chronic hepatitis B infection were gathered from various databases,including SinoMed,Chongqing Vip,China National Knowledge Infrastructure,Wanfang,the Cochrane Library,PubMed,Web of Science and Embase.Excel 2019 was utilized to establish a database of acupuncture prescriptions and conduct statistics on the frequency,meridian application,distribution and specific points,as well as SPSS Modeler 18.0 and SPSS Statistics 26.0 to conduct association rule analysis and cluster analysis to investigate the characteristics and patterns of acupoint selection.Results:A total of 42 studies containing 47 acupoints were included,with a total frequency of 286 acupoints.The top five acupoints used were Zusanli(ST36),Ganshu(BL18),Yanglingquan(GB34),Sanyinjiao(SP6)and Taichong(LR3),and the most commonly used meridians was the Bladder Meridian of Foot-Taiyang.The majority of acupuncture points are located in the lower limbs,back,and lumbar regions,with a significant percentage of them being Five-Shu acupoints.The strongest acupoint combination identified was Zusanli(ST36)–Ganshu(BL18),in addition to which 13 association rules and 4 valid clusters were obtained.Conclusion:Zusanli(ST36)–Ganshu(BL18)could be considered a relatively reasonable prescription for treating chronic hepatitis B infection in clinical practice.However,further high-quality studies are needed.

Innovative data mining approaches for outcome prediction of trauma patients

Trauma is the most common cause of death to young people and many of these deaths are preventable [1]. The prediction of trauma patients outcome was a difficult problem to investigate till present times. In this study, prediction models are built and their capabilities to accurately predict the mortality are assessed. The analysis includes a comparison of data mining techniques using classification, clustering and association algorithms. Data were collected by Hellenic Trauma and Emergency Surgery Society from 30 Greek hospitals. Dataset contains records of 8544 patients suffering from severe injuries collected from the year 2005 to 2006. Factors include patients' demographic elements and several other variables registered from the time and place of accident until the hospital treatment and final outcome. Using this analysis the obtained results are compared in terms of sensitivity, specificity, positive predictive value and negative predictive value and the ROC curve depicts these methods performance.

Improvement of Mining Fuzzy Multiple-Level Association Rules from Quantitative Data

Data-mining techniques have been developed to turn data into useful task-oriented knowledge. Most algorithms for mining association rules identify relationships among transactions using binary values and find rules at a single-concept level. Extracting multilevel association rules in transaction databases is most commonly used in data mining. This paper proposes a multilevel fuzzy association rule mining model for extraction of implicit knowledge which stored as quantitative values in transactions. For this reason it uses different support value at each level as well as different membership function for each item. By integrating fuzzy-set concepts, data-mining technologies and multiple-level taxonomy, our method finds fuzzy association rules from transaction data sets. This approach adopts a top-down progressively deepening approach to derive large itemsets and also incorporates fuzzy boundaries instead of sharp boundary intervals. Comparing our method with previous ones in simulation shows that the proposed method maintains higher precision, the mined rules are closer to reality, and it gives ability to mine association rules at different levels based on the user’s tendency as well.

Spatial Multidimensional Association Rules Mining in Forest Fire Data

Hotspots (active fires) indicate spatial distribution of fires. A study on determining influence factors for hotspot occurrence is essential so that fire events can be predicted based on characteristics of a certain area. This study discovers the possible influence factors on the occurrence of fire events using the association rule algorithm namely Apriori in the study area of Rokan Hilir Riau Province Indonesia. The Apriori algorithm was applied on a forest fire dataset which containeddata on physical environment (land cover, river, road and city center), socio-economic (income source, population, and number of school), weather (precipitation, wind speed, and screen temperature), and peatlands. The experiment results revealed 324 multidimensional association rules indicating relationships between hotspots occurrence and other factors.The association among hotspots occurrence with other geographical objects was discovered for the minimum support of 10% and the minimum confidence of 80%. The results show that strong relations between hotspots occurrence and influence factors are found for the support about 12.42%, the confidence of 1, and the lift of 2.26. These factors are precipitation greater than or equal to 3 mm/day, wind speed in [1m/s, 2m/s), non peatland area, screen temperature in [297K, 298K), the number of school in 1 km2 less than or equal to 0.1, and the distance of each hotspot to the nearest road less than or equal to 2.5 km.

Rules of Meridians and Acupoints Selection in Treatment of Parkinson’s Disease Based on Data Mining Techniques

Objective:To help selecting appropriate meridians and acupoints in clinical practice and experimental study for Parkinson’s disease(PD),the rules of meridians and acupoints selection of acupuncture and moxibustion were analyzed in domestic and foreign clinical treatment for PD based on data mining techniques.Methods:Literature about PD treated by acupuncture and moxibustion in China and abroad was searched and selected from China National Knowledge Infrastructure and MEDLINE.Then the data from all eligible articles were extracted to establish the database of acupuncture-moxibustion for PD.The association rules of data mining techniques were used to analyze the rules of meridians and acupoints selection.Results:Totally,168 eligible articles were included and 184 acupoints were applied.The total frequency of acupoints application was 1,090 times.Those acupoints were mainly distributed in head and neck and extremities.Among all,Taichong(LR 3),Baihui(DU 20),Fengchi(GB 20),Hegu(LI 4)and Chorea-tremor Controlled Zone were the top five acupoints that had been used.Superior-inferior acupoints matching was utilized the most.As to involved meridians,Du Meridian,Dan(Gallbladder)Meridian,Dachang(Large Intestine)Meridian,and Gan(Liver)Meridian were the most popular meridians.Conclusions:The application of meridians and acupoints for PD treatment lay emphasis on the acupoints on the head,attach importance to extinguishing Gan wind,tonifying qi and blood,and nourishing sinews,and make good use of superior-inferior acupoints matching.

A Data Mining-Based Study on Medication Rules of Chinese Herbs to Treat Heart Failure with Preserved Ejection Fraction

Objective:To summarize the medication rules of Chinese herbs to treat heart failure with preserved ejection fraction(HFPEF)based on data mining and to provide references for clinical utilization.Methods:The China National Knowledge Infrastructure(CNKI),Wanfang database(Wanfang),VIP database(VIP),Chinese Biomedical Literature(CBM),PubMed,Embase,and Cochrane Library databases were searched from inception to October 2021 to identify relevant literature on treating HFPEF with Chinese herbs.Microsoft Excel 2019 was used to set up a database,and then,association rule analysis and hierarchical cluster analysis were performed by using apriori algorithm and hclust function respectively in R-Studio(Version 4.0.3).Results:A total of 182 qualified papers were included,involving a total of 92 prescriptions,130 Chinese herbs,and 872 individual herbs prescribed,with an average of 9.5 herbs per prescription.The six most frequently prescribed herbs were Astragali Radix(Huangqi),Salviae Miltiorrhizae Radix Et Rhizoma(Danshen),Poria(Fuling),Glycyrrhizae Radix Et Rhizoma(Gancao),Cinnamomi Ramulus(Guizhi),and Ginseng Radix Et Rhizoma(Renshen).There were 35 herbs used more than 5 times,involving 11 efficacy categories.The top three categories were deficiency-tonifying herbs,blood-activating and stasis-removing herbs,and dampnessdraining diuretic herbs.The most commonly used herbs were mainly warm and sweet.The primary meridian tropisms were Lung Meridian,Heart Meridian and Spleen Meridian.Association rule analysis yielded 26 association rules,such as Astragali Radix(Huangqi)&Salviae Miltiorrhizae Radix Et Rhizoma(Danshen),Poria(Fuling),Cinnamomi Ramulus(Guizhi)&Atractylodis Macrocephalae Rhizoma(Baizhu).Hierarchical cluster analysis yielded four herb classes,and their functions were mainly qi-replenishing and yang-warming,bloodactivating and diuresis-inducing.Conclusions:HFPEF is the syndrome of root vacuity and tip repletion,and its core pathogenesis is"deficiency","stasis",and"wafer",with"deficiency"being the most principal,which is closely related to Xin(heart),Fei(Lung),and Pi(Spleen).The treatment of this disease occurs by improving qi,warming yang,activating blood and inducing diuresis.Astragali Radix(Huangqi)with Salviae Miltiorrhizae Radix Et Rhizoma(Danshen)is the basic combination of herbs applied.

Mining multilevel spatial association rules with cloud models

The traditional generalization-based knowledge discovery method is introduced. A new kind of multilevel spatial association of the rules mining method based on the cloud model is presented. The cloud model integrates the vague and random use of linguistic terms in a unified way. With these models, spatial and nonspatial attribute values are well generalized at multiple levels, allowing discovery of strong spatial association rules. Combining the cloud model based method with Apriori algorithms for mining association rules from a spatial database shows benefits in being effective and flexible.