肝硬化数据挖掘

ComparisonofAITechniquesforPredictionofLiverFibrosis

inHepatitisPatientsJournalofMedicalSystemJiajunShiSomeexplanationsFibrosis-纤维化Hepatitis-肝炎HepatitisB/C–乙肝/丙肝Cirrhosis–肝硬化Liverbiopsies-活组织检查Non-invasivetechniques–无创技术Serummarkers–血清标记

OutlineIntroductionBackground:AIandCDSSNaïveBayesClassifier(NBC)&LogisticsRegressionHepatitisandFibrosisStageAIAssistedWeb-basedClinicalDecisionSupportSystemFourMethodsResultsandDiagnosticAccuracyConclusionIntroductionOneintwelvepeoplehavetheHepatitisBorHepatitisCvirusDiagnosisandtreatmentofthisdiseaseisguidedbyliverbiopsieswhereasmallamountoftissueisremovedbyasurgeonandexaminedbyapathologistDeterminethefibrosisstagefromF0(nodamage)toF4(cirrhosis)RiskandcostlyNon-invasivetechniques,withserummarkers,imagingtest,andgeneticstudiesAccuracynotachievedsufficientacceptanceIntroductionNon-invasivetechniques,withserummarkers,imagingtest,andgeneticstudiesAI

&CDSSKnowledgeofthelevelofliverdamageinapatientwith

liverdisease(particularlyHepatitisBandHepatitisC)isa

criticalfactorindeterminingtheoptimalcourseoftreatment

andtomeasuretheeffectivenessofalternativetreatmentsin

patients.NotaccurateBackgroundofAIandCDSSArtificialIntelligenceandDataMiningtechniquesIncludeNeuralNetworks,FuzzyLogic,DecisionTrees,BayesianClassifiers,SupportVectorMachines,GeneticAlgorithmsandHybridSystemClinicalandMedicalDecisionSupportSystemsSupporttheprocessofdiscoveringusefulinformationinlargeclinicalrepositoriesTheyhaddonethesystemdesignedwithneuralnetworksanddecisiontreemethodsbecauseoftheirsuccessfulapplicationinsimilarproblemdomainsHepatitisandFibrosisStageOneintwelvepeoplehavetheHepatitisBorHepatitisCvirusFibrosisStage

Description0Nofibrosis-Normalconnectivetissue

1Portalfibrosis-Fibrousportalexpansion

2Periportalfibrosis-Periportalorrareportal-portalsepta

3Septalfibrosis-Fibrousseptawitharchitecturaldistortion;no

obviouscirrhosis

4Cirrhosis

AIAssistedWeb-basedClinicalDecisionSupportSystemAIAssistedCDSSAItechniquesResultingknowledgebaseAIAssistedWeb-basedClinicalDecisionSupportSystemExplanations血清细胞碱性磷酸酶血清胆碱酯酶胆红素谷氨酸转肽酶丙种球蛋白类测试时年龄乙肝or丙肝Variables：SerumMarkersPatientsInfoAIAssistedweb-basedClinicalDecisionSupportSystemSysteminputs&Outputs:FourMethodsPaper‘AdvancedDecisionSupportforComplexClinicalDecisions’NeuralNetworks,DecisionTreesThispaperNaiveBayesandLogRegressionMethodinputs:FourMethods–NaïvebayesclassifierThevariationinmeanvaluesfortwoparameters(ABLandG-GL)areshownbyfibrosisstageintheFigure.Withthismodel,wecancalculatethecombinedprobabilityofeachfibrosisstagethenselectthehighestprobableasourpredictedresult.FourMethods-LogisticsregressionCrossValidationandDiagnosticAccuracyCrossValidationandDiagnosticAccuracyAccuracyofFibrosisStagePredictions(424patients)

PredictiveSensitivityandSpecificityConclusionThefourartificialintelligencemethodspresentedinthisstudyshowedsomesignificantvariabilityinaccuracy,sensitivity,andspecificityinpredictingfibrosisstageindataon424hepatitispatients.Althoughneuralnetworkmethodsshowedthehighestsensitivityandspecificity,theirroleispredictingtheexactfibrosisstagewasrelativelypoor.Logisticregressionandnaïvebayesmethodswereth