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ComparisonofAITechniquesforPredictionofLiverFibrosis

inHepatitisPatientsJournalofMedicalSystemJiajunShiSomeexplanationsFibrosis-纖維化Hepatitis-肝炎HepatitisB/C–乙肝/丙肝Cirrhosis–肝硬化Liverbiopsies-活組織檢查Non-invasivetechniques–無創(chuàng)技術(shù)Serummarkers–血清標(biāo)記

OutlineIntroductionBackground:AIandCDSSNa?veBayesClassifier(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血清細胞堿性磷酸酶血清膽堿酯酶膽紅素谷氨酸轉(zhuǎn)肽酶丙種球蛋白類測試時年齡乙肝or丙肝Variables：SerumMarkersPatientsInfoAIAssistedweb-basedClinicalDecisionSupportSystemSysteminputs&Outputs:FourMethodsPaper‘AdvancedDecisionSupportforComplexClinicalDecisions’NeuralNetworks,DecisionTreesThispaperNaiveBayesandLogRegressionMethodinputs:FourMethods–Na?vebayesclassifierThevariationinmeanvaluesfortwoparameters(ABLandG-GL)areshownbyfibrosisstageintheFigure.Withthismodel,wecancalculatethecombinedprobabilityofeachfibrosisstagethenselectthehighestprobableasourpredictedresult.FourMethods-LogisticsregressionCrossValidationandDiagnosticAccuracyCrossValidationandDiagnosticAccuracyAccuracyofFibrosisStagePredictions(424patients)

PredictiveSensitivityandSpecificityConclusionThefourartificialintelligencemethodspresentedinthisstudyshowedsomesignificantvariabilityinaccuracy,sensitivity,andspecificityinpredictingfibrosisstageindataon424hepatitispatients.Althoughneuralnetworkmethodsshowedthehighestsensitivityandspecificity,theirroleispredictingtheexactfibrosisstagewasrelativelypoor.Logisticregressionandna?vebayesmethodswereth

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