RESUMEN
Objective To use feature selection and Likert grading method to quantify the data of lung cancer medical records,to construct a deep extreme learning machine model optimized by the sparrow search algorithm,to classify and predict the syndrome types of traditional Chinese medicine medical record data of lung cancer,and to provide scientific and effective research on syndrome type classification of traditional Chinese medicine.means.Methods The medical records of 497 cases diagnosed with lung cancer from January 2015 to December 2021 were collected from the Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine,and 412 medical records were screened as the research objects.Syndromic factors of different syndromes were summarized by feature selection and feature importance ranking,and the syndrome factors were quantified by Likert grading method.Build a deep extreme learning machine optimized based on the sparrow search algorithm,and train and test the model.Finally,the model built in this paper is compared with other machine learning models according to three evaluation criteria.Results The average classification accuracy of the SSA-DELM model established in this paper was 88.44%,while the average accuracy of the support vector machine and Bayesian network was 83.39%and 84.53%,respectively.The recall rate and F1 value of the SSA-DELM model on the five syndrome types are mostly above 80%,which is also better than other traditional machine learning models.Conclusion The results of the study show that the use of feature selection combined with Likert grading method to quantify the lung cancer medical record data,compared with the 0-1 processing data,can show the characteristics of the data,improve the accuracy of the classification model,SSA-DELM new Compared with other traditional machine learning classification models,the model has better representation learning ability and learning speed.This model not only provides a scientific and technical means for the clinical treatment of lung cancer,but also provides a useful reference for the informatization and intelligent development of TCM syndrome differentiation and treatment.
RESUMEN
OBJECTIVE：To comprehensi vely evaluate the application progress of network pharmacology methods in TCM compound prescription research ，and to provide reference for modernization of TCM compound prescription research. METHODS ： Taking“network pharmacology ”and“TCM compound prescription ”as keywords ，the literatures were retrieved from CNKI ， Wanfang database and VIP during May 2006 to May 2020. Screening literature ，the databases ，analysis platforms ，and software used of the literature were summarized ；on the basis of quantitative analysis ，the application of network pharmacology in the research of traditional Chinese medicine compound were summarized. RESULTS & CONCLUSIONS ：There were a total of 761 valid literatures ，among which the number of literatures that could be retrieved in 2019 reached 313. In the modernization research of TCM compound prescription ，network pharmacology methods were mainly usedmechanism ，material basis of pharmacodynamics ， compatibility law ，compound optimization ，and“effect-toxic”network. Commonly used databasesand platforms included traditional chinese medicine information database （TCMSP and TCMID ），therapeutic target database （TTD，OMIM），drug targets and target prediction platform （Drugbank，SwissTargetPrediction，TargetNet，PharmMapper），network pharmacology analysis and prediction software and platform （CytoScape），etc. Network pharmacology method was widely used in the field of TCM compound prescription research ，and provided new ideas and methods for the modernization of TCM compound prescription research. In the future，the related research can be combined with the pharmacokinetic parameters ，the efficacy of active compounds and related basic experiments ，use the weighted method to carry out network pharmacology analysis ，and integrate the information of multiple databases to improve the scientificity of research results.
RESUMEN
Objective Toinvestigatethechangesofiron,fatandwatercontentinspleentissuesforacutepancreatitis(AP).Methods Atotal of44patientswithAP(experimentalgroup)and21healthysubjects(controlgroup)wererecruitedinthisstudy.RoutineupperabdominalMR scansandIDEAL-IQsequencescanwereperformed.TheR2?,Water,FatandFFvaluesofspleenwererespectivelymeasuredinthe experimentalgroupandcontrolgroup,andthedataofthetwogroupswereanalyzedstatistically.Results TheR2?value(P=0.011),Water value(P=0.003)andFatvalue(P=0.022)ofspleenintheexperimentalgroupandthecontrolgrouphadsignificantdifferences, whiletheFFvalue(P=0.861)didn’t.TherewerenosignificantdifferencesinR2?,WaterandFatvaluesinthemild,moderateand severeAP (P>0.05).aswellasintheyounggroup (14-44yearsold),themiddle-agedgroup (45-59yearsold)andtheelderly group (≥60yearsold)inAP (P>0.05).Conclusion APcanleadtothechangesofirondeposition,fatandwatercontentinspleen tissue,andIDEAL-IQtechnologycanquantitativelyevaluatethechangeofthem.
