艾昆纬-人工智能和机器学习助力中国医疗保健：一种算法驱动的方法

WhitePaperArtificialIntelligenceandMachineLearningEmpowersHealthcareinChina:anAlgorithm-DrivenApproachJointlyIssuedbyIQVIADataScience&AdvancedAnalyticsteamandBeijingZGCArtificialIntelligenceTechnologyDevelopmentCo.LtdArtificialIntelligenceandMachineLearningealthcareinChinamDrivenApproachuthorseVicePresidentDataScienceandAdvancedAnalyticsIQVIAHuiJin,SeniorDirector,DataScienceandAdvancedAnalytics,IQVIAYueWang,SeniorConsultant,DataScienceandAdvancedAnalytics,IQVIADeputyGeneralManagerBeijingZGCArtificialIntelligenceTechnologyDevelopmentCo.LtdSupportedbyYisiYuSugeWangYanxinYang,ChenxiYang,HanyuGao,ChuchuLiuYuboHeDaozhouYaoBingzhenWuTolearnmoreaboutwhitepaper,pleaseContact:SeniorDirectorDataScienceandAdvancedAnalytics,IQVIAcomDeputyGeneralManager,BeijingZGCArtificialIntelligenceTechnologyDevelopmentCo.Ltd.leofContentsExecutiveSummary02PartI:MarketOutlookforChina'sHealthcareIndustry031.1MacroEnvironment031.2IndustryandIndustryEnvironment03PartII:Algorithm-DrivenApproach:AdvancedAnalyticsfor05euticalEnterprisesintheDigitalEra2.0CoreView:AI/MLTechnologyUsesData,Experience,andAlgorithmstoEmpower06BusinessDecisionsandHelpEnterprisesReduceCostsandIncreaseEfficiencythroughouttheProductLifecycle2.1LeadingSpeed:AI/MLLearningTechnologyEnhancesBusinessAgility06CaseStudy1:Clinicaltrialoptimization06CaseStudy2:COVID-19Impactforecastingin2022072.2PreciseDecision:AI/MLTechnologytoImproveDecisionAccuracy08CaseStudy1:Rarediseaseprediction08CaseStudy2:Omni-channelmarketingintelligentrecommendationsystem082.3EasyScalability:AI/MLTechnologyProvidesSolutionScalability09CaseStudy1:Salesforecastingplatform09CaseStudy2:Adversedrugeventalertplatform102.4DeepInsights:AI/MLTechnologyEmpowerScientificDecision10CaseStudy1:Sociallistening10CaseStudy2:Causalanalysismodeldrivesmarketingdecisions11PartIII:LookingforPartners12Conclusion14RArtificialIntelligenceandMachineLearningarivenApproachfactorswhicharechallengingtraditionalmarketingdistributionchannelsandreshapingthedemand-perceptionB2BandB2Cmarkets,forcingenterprisestosstrategiestocopewiththechangesEnvironmentalpolicyhasprovidedregulatoryguidancetopromotestructuralreformofthehealthcareindustry’ssupplychain.Procurement(VBP)toboostartificialintelligence(AI)inthehealthcareindustry,facilitatingenterprisestoreducecosts,increaseefficiency,anddriveinnovation.Inthetechnicalenvironment,wherenewtechnologiessuchasbigdataandAIhaveadvancedndustryhaveprovidednewimpetusforgrowthcareindustryasadrivingforcetoimprovehealthcarerelatedproductszantughtbyartificialintelligenceandmachinelearningAIMLtotheRDproductionanddistributionofproductseapplicationscenariosfromthesetechnologiesAI/MLtechnologiescanenhancebusinessagilityandimprovestrategiesbyincreasingfast,accuratedecision-making,reducingcoststhroughreadyscalability,andfacilitatingbusinessdecision-makingthroughin-depthinsight.AI/MLbringschangestotherspectives1)Drugdiscovery:AI/MLtechnologybuildsalgorithmicmodelsbasedonscientifictheoriestoacceleratetargetdiscovery,leadeinthelaboratoryaltrialprocessoptimaloutreach,andflexiblestrategyadjustmentplansleveragingmassivedataandcontinuousiterationofadvancedmodelalgorithms.algorithmsinthehealthcareindustryhighlydependsonenterprisesandtheirpartnersgyTofullyintegratethesetechnologieswithocationswithstrongcomputkforChinasHealthcareIndustrytotheChinesehealthcareindustry,challengingthegrowthandprofitabilityofpharmaceuticalenterprises.AI/MLtechnologycanhelpenterprisesreducecostsandincreaseefficiencyinexistingprocedures,providenewgrowthimpetusandachievealgorithm-drivengrowth.ntCoreviewTheCOVIDpandemichasbroughtaninescapableimpactonboththedemandandsupplysideofthehealthcareindustrysopharmaceuticalenterprisesneedtoimprovetheirmarketstrategiestocopewiththesechanges.TheCOVIDpandemichaschallengedpharmaceuticalandmedicaldeviceenterprisesR&D,production,andsupplychainstability.sofstagnantclinicaltrialprocessesetothespreadofCOVIDInclinicalresearchanddevelopmentCOVID-19impedespatientresearchInadditionthepandemicalsoposesathreattothesupplychaincapabilitiesoftheupstreamanddownstreamindustries;enterprisesareconfrontedwithbottleneckissuesforrawmaterials,productsinstorageandtransportationconsequentlyescalatingsupplychaincosts.Thepandemichasreformedthetraditionalmarketingoutreachchannelsofthehealthcareindustry.rtheBBmarkettheinteractionbetweendifferentplayersinthehealthcaresystemhassignificantlychanged,wherephysician-repre-sentativeandphysician-patientinteractionsshiftmoretoonlinechannelsTraditionalface-to-facevisitsbymedicalrepresentativesprisesneededtoconductcommunicationwithhealthcareprofessionalsthroughreshapedtheinformationaccesschannelsandhealthconsumptionntialconsumersWithasignificantincreaseinonlinebrowsingtimeconsumersaremoreinclinedtoobtainhealthcarenghealthknowledgethroughWeChatofficialaccountsand%acquiringhealthinformationthroughAPPs,onlineforumsandwebsitesWiththeincreasingaccessibilityofmedicalproductsandservicespatienttreatmentproceduresinareassuchaschronicngDoctor2022).Inshort,theCOVID-19pandemichasreconstructedtheinformationaccesschannelsandbusinessoperationsinbothgrowthofpharmaceuticalenterprises.Thepandemichasreshapedtheperceptionofdemandinthehealthcaremarket.OntheonehandthepandemichasacceleratedmedicalinsurancecostcontrolinvestmentinthemedicalinsurancefundinpandemicpreventionandcontrolhasputpressureonthegrowthandprofitofnonCOVIDrelatedproductsforpharmaceuticalenterprises.Ontheofhealthcarepromotingahealthylifestyle."Consumershavebecomeincreasinglyawareoftheconceptofhealthandwell-being,verseanddetaileddemandsforpharmceuticalcompaniesinsegmentsinlcudingoralcareandaestheticmedicine.oreviewMedicalsystemreformdrivespharmaceuticalenterprisestoreducecostsincreaseefficiencyanddeployinnovation.ltipleeffortsareneededtoimprovethequalityandspeedofdrugRDandunleashpharmaceuticalenterprises’innovativepotential.TheNationalMedicalProductsAdministration(NMPA)continuestoestablishandimprovedrugreviewstandardsystemstopromotepatientcenteredscientificandorganizedR&Dofhigh-qualitypharmaceuticalproductsNMPAisdevotedtospeedingupproductapprovalandlaunchprocedurestoimprovethemarketaccessibilityofinnovativeproductsandclinicallyurgentproducts.Inrecentyears,NMPAhasissued1guidelinesfordrugandmedicaldeviceR&D,coveringchemicaldrugsbiologicalproductsandotherfields,providingscientificandstandardisedguidancefordrugandmedicaldeviceenterprisesinproductdevelopmentproduction,approval,andmarketingNMPA2022a).Atthesametime,NMPAfocusesonacceleratingdrugreviewandapprovalprocesses,settingupfasttracksforbreakthroughtreatmentprocedures,conditionalapprovalprocedurespriorityreviewandapprovalprocedures,andspecialapprovalprocedurestoacceleratedrugdevelopmentandlaunchNMPA22a).In2021,NMPAachieveda98.93%overalldrugapprovalcompletionrateahistoricbreakthrough(NMPA,RDcapabilitiesEnterprisesneedtofinduniquecompetitivenessrationsshortlifecycles,lowmarginsandmoreintensecompetition.rvolumebasedprocurementstrengthensthesupplyandaccessibilityofdrugsandmedicaldevices.In-depthadjustmentonthedrugsupplysecuritysystemhassignificantlyimprovedaccessibility.Since2018,theChinesegovernmenthassecureddrugandmedicaldevicesuppliesbypilotingandpopularizingVBPfordrugsandhigh-valuemedicalconsumablesimprovedtheNationalEssentialMedicineSystemNEMS,andnormalizedannualadjustmentonthelistofmedicinestyofessentialdrugsanddevices.BytheendofJuly2022,theNationalHealthcareSecurityAdministration(NHSA)hadcarriedoutsevennationalroundsofVBP,covering294drugvarieties.NHSAexpectstocontinueregularized,institutionalizedandstandardizedVBPwhileexpandingthelistandspeedinguptheprocedure,withacomrtingMedTech•InJuly2020,NDRCissuedtheGuidelinesforConstructingNext-GenerationAIStandardtostandardizetop-leveldesignforAI,promoteAIR&D,andprioritizeAIapplicationinhealthcare.•InJuly2017,theStateCouncilissuedthe“Next-GenerationAIDevelopmentPlan"topromotetheapplicationofnewmodelsandmethodsofAI-poweredmedicaltreatmentsystems.iagnosisregulationInApriltheNationalHealthmissionreleasedNationalRestrictedTechnology22,RegulationforAIassistedTherapy,andQualityControlIndicatorsforlinicalApplicationofAIassistedTherapy22ssisteddiagnosistechnologywasremovedfromthelistofrestrictedtechnologiesboostingAIapplicationinthehealthcareindustrycoveringmagerecognitionpathologicaldiagnosis,etc.theNationalHealthCommissionplanstodynamicallyadjustandoptimizeNEMSandcontinueimprovingthecatalogadjustmentmanagementmechanism.Theywillconductregularadjustmentsofthenationalessentialdrugcatalogonceeverythreeyearsbyintegratingfactorssuchastheclinicalapplicationpracticeofdrugs,changesindrugstandardsandnewdruglaunches(GeneralOfficeicyfeaturedinVBPpolicyhaspushedenterprisestosignificantlyreducedrugandmedicaldevicepricesforthein-hospitalmarket,creatinglongtermchallengesfortheprofitsandgrowthofpharmaceuticalenterprises.tedmedicalsystemreformfacilitatespharmaceuticalsupply-sidereform.medicalsystemreform.Supply-sidereformofdrugsandmedicaldevicesintertwinescloselywiththereformofstate-ownedhospitalstofacilitaterationaldruguseanddiagnosis/treatmentstandardizationbecomingthecoreofthesteadydevelopmentofmedicalsystem.Improvingthequalityandreducingthepriceofdrugsandmedicaldevicesarealsocloselyintegratedwithmedicalinsurancepaymentreform.TheDRG/DIPreformwillstartin2022,andisexpectedtobeintroducednationwidein2024andachievefullcoveragebytheendof2025(NationalMedicalSecurityAdministration,2021).Inthenewerafeaturingmedicalinsurancefundcontrolmeasures,comprehensivemedicalinsurancepaymentreformandstate-ownedhospitalreform,pharmaceuticalandmedicaldeviceenterpriseswillfacebothopportunitiesandchallenges(Figure1.1)ureArtificialIntelligenceMedicalPoliciesngAIdeviceregistrationInMarchNMPAissuedGuidelinesforAIMedicalDeviceSoftwareRegistrationandGuidelinesforMedicalDeviceCybersecurityRegistrationasnormativeguidanceforAImedicalsoftwareregistrationInJulyNMPAissuedGuidinginciplesforAIMedicalSoftwareoductClassificationandDefinitionidingAImedicalsoftwareccordingtothematurityofalgorithmapplicationasClassIIorClassIII.SourcePublicinformation,collectedbyIQVIAnologyenvironmentCoreviewAdvancednewtechnologiessuchasbigdata,AI,andMLofferrichapplicationinhealthcareindustry,creatinggrowthpotentialtawithlargevolumeshighstructuraldiversityandfastgrowthinthehealthcaremarketdependsonAIandMLtechnologiesforvalue-addedanalysisandresearch.TraditionaldigitalanalyticscanhardlyuncoverthetruevalueofthemassiveamountofstructuredandunstructuredinformationinthedecisionmakingAIreferstosystemsandmachinesthatcanmimichumanintelligencetoperformtasksanditerativelyimprovethemselvesbasedontheinformationcollected.MLisapartofAItechnologythatcanbeimplementedthroughstatisticalmodelsandmethodsthatallowcomputerstoimprovepredictiveorexplanatorycapabilitiesthroughexperienceaccumulation(IBM,2022).Theindustrycanhelpenterprisesreducecostsandmakebetterbusinessdecisions.AI/MLtechnologyisincreasinglyappliedinvarioushealthcareprocessesCuttingedgeAI/MLtechnologyfeaturedbycomputervisionCV,naturallanguageprocessing(NLP)andmachinelearningMLhavewidelypenetratedvariousstructuresinthehealthcareindustryasadrivingforceforimprovinghealthcareservices.ntyearstheacceleratedmaturationofAItechnologyhasenricheditsapplicationsinthehealthcarefieldAtpresent,theapplicationofAItechnologycoversmedicalimageprocessing,clinicaldecisionsupportsystemCDSSprecisionmedicine,healthroboticsAIMLdrivenhealthcareapplicationsarededicatedtohelpingreducecostsandincreaseefficiencyimprovingdiagnosisandtreatmentenhancingpatientexperiencereducingdiseaserisksandcomprehensivelyempoweringprehospitalin-hospital,andpost-hospitalpatientexperience.(Figure1.2).eArtificialIntelligenceandMachineLearningTechnologyningubsetofningubsetofAItechniqueswhichusestatisticaldstoenablemachinestoimproveesultwithexperienceINTELLIGENCEArtificialIntelligencenguagegAnynguagegmputerstomimicintelligenceMACHINEubsetofmachinelearningthatdrivespervisedlearningfromdatathatructuredorunlabeledsedageProcessingingthatallowscomputerstounderstandanlanguageasitisspokenorwrittenSourcepublicinformation,collectedbyIQVIAtryEnvironmentryntothereFigureSummaryandOutlookofAITechnologyApplicationScenariosdapplicationstationsdevelopmentAI+PrecisionMedicineAI+DeviceR&DAI+DeviceR&DAI-assisteddiagnoseCNNs,TransformerdicineNaveBayesSVMDLCDSSAI+ComplianceMinutesVideomonitorAEdetectionAI+PublicHealthInfectionpreventionAI+HealthManagementAIassistantWearabledeviceAI+HospitalManagementSmartEMRAItriageSmartEMRAItriagesemanticdeterminationSpeechrecognition,NLU,randomforestSourcepublicinformation,collectedbyIQVIASurgicalassistanceSurgicalrobotDevicequalityinspection3Drebuilt,objectdetection,semanticsegmentationMotion2Vec,Multi-taskSHNOCR,imagerecognition,anomalydetectionAI+PharmaDrugdiscoveryDrugResponsePredictionAI+PlasticSurgeryPersonalizedplasticsurgeryPlasticsurgeryeffectdisplayAI+SmartContractSmartinsuranceclaimBlockchainEMRandtionAI+PrivacyGANs,diffusionmodelsPrivacyGANs,diffusionmodelsPrivacythreat,attackerandsecuritymodelSecuremulti-partycomputingHomomorphicencryption,differentialprivacyFederatedlearning3DrebuiltandprintingEMGsensorarray,reinforcementlearningPredictivemodel,imagerecognition,semanticsegmentation,objecttrackingDigitalHealthProstheticsOrgan-on-a-chipAI+FutureConceptbrain-computerinterfaceHumandigitaltwinSignalprocessing,transferlearning,deeplearningMultidisciplinarymodeling,bigdataanalysis,ultra-performancecomputing,machinelearning,simulationApplicationScenarioacomCoreCoreView:AI/MLTechnologyUsesData,Experience,andAlgorithmstoEmpowerBusinessDecisionsandisesReduceCostsandIncreaseEfficiencythroughouttheProductLifecyclenmentandrapidlychangeablerovetheirabilitytorespondsesandmodelsinordertomeeterprisesreducecostsimproveigitaltransformationFigureyleadcompoundzemassclinicaltrialdatatstherebyacceleratingtheralleleddataandadvanceddictionandoptimalreachstrategydesignFigureAIMLTechnologyEmpowersMultipleApplicationsthroughouttheProductLifecycleCLINICALCLINICALSOLUTIONSCOMMERCIALIZATIONDrugdiscoveryPre-clinicalClinicaltrialsPre-launchLaunchMaturityLossofexclusivityPre-clinicalClinicaltrialRealworldComplianceforceDigitalAdverseservicesrecruitmentoptimizedevidenceservicesreadinessoptimizedaccessmarketingliaisoneventsTopTopcommercialsolutionsAIapplications40+commercialofferings,frompre-launchtoLOE•Predictiveanalytics(diseasedetectionandprogression,LOTT,etc.)•HCPsegmentationandtargeting(nextbestHCP,etc.)Multichannelmarketingoptimizationnextbestchannel)tiindicationproductanalyticsndoptimizerTopclinicalsolutionsAIapplicationsOptimizingandautomatingcoreclinicalserviceslplanningtigatorrecruitmententrecruitmentcyclesafetyClinicalriskbasedandcentralizedmonitoringTopTopAIsoftwareapplicationsEnablingandoptimizingclinicalandcommercialservices,atscaleClinicalnaturallanguageprocessingtranslationalscience,R&D,clinicaloperations)•SaaSAIcommercialplatform(HCPsegmentationandtargeting,multi-indication,patientjourney,etc.)•Adacommercialengine(nextbestcustomer,NBmessage,marketingbudgetoptimizer,etc.)ourceIQVIAinternalmaterialsuicklyandcomprehensivelytoobtainfeedbackbasedonvaluableanalysistoadjustbusinessstrategiesinatimelymanner.TraditionalbusinessanalysisoftenfailstomeetctiondjustmentsandenhancebusinessagilityeStudyClinicaltrialoptimizationrateacrossalldeclinedtoinientificriskinacyandsafetyevelopmentduetocessvarieselowtheiovascularandlonglopmentedModeldelnCoverageedModeldelnCoverageContainmentIndex3.Population&TransportationData4.Other....VariousPublicPtOutpatientPt#LoadRecovery4.Other....eForecastingFullYearData–2022.03)ntLoadgCOVIDSimulationmodelMonteCarloofoutbreakinrestofLockdownDurationntensityofLockdown.ImpactonmarketationmulatedImpactonaPharmaMarketeForecastingFullYearmenttimelationsivedatangstudiesigureAIMLTechnologyEmpowersClinicalTrialsOptimizationtdsubpopulationBiomarkersWithDesirableOutcomemarkersWithIndifferentOutcomepulationanalysisrceIQVIAinternalmaterialsswithUndesirableOutcomestsswithUndesirableOutcomestswithDesirableOutcomesubpopulationBiomarkerswithIndifferentOutcomesCaseStudyCOVIDImpactforecastingin2022einghitbytheessandPrimaryicalmarketbilityoffuturengMontesedonIQVIAsentfeedbackngesinmarketmprehensivernextbusinessplansFigureigureAIMLTechnologyEnableForecastingPandemicTrendAssumptions:11pDynamicZeroCOVIDPolicyandNormalizedCOVIDContainmentsnewVOCSpreadingOmicronVariantRemainsMainstream22VIDMedicineLaunchedcastingperiodwithin3344ToToforecastbaselineresultsbyprovinceunderdynamiczero-COVIDpolicyTosimulatefutureimpactbyprovinceandaggregatedtototalChinapharmamarketurceIQVIAinternalmaterialsacomPatientsPopulationatPatientsPopulationatriskPreciseDecisionAIMLTechnologytoImproveDecisionAccuracycalreformmeasurespharmaceuticalandmedicaldeviceenterprisesneedtotakebecognizantofmarketchanges,reasonablyoptimizetheirdecision-makingmodels,andgenerateaccuratemarketinsightstodevelopoptimalegiesInrecentyearswiththesignificantincreaseinthequantityandqualityofdatainthehealthcaremarketAIMLofdecisionsupportingprocessesbasedonAI/MLalgorithmswithknowledgeinthemedicalfieldcansignificantlyimprovedecision-makingaccuracyinareassuchasdiseasediagnosis,pharmacovigilanceandmedicalinformationcommunication.eStudyRarediseasepredictiondiseasesarecharacterisedbylowclinicalcasedatacomplexdiseasetypesandhighmisdiagnosisrate.Accuratediagnosisofrarediseaseshasalwaysbeenacommonchallengefordoctorsandpatients.WiththehelpofAI/MLalgorithms,IQVIA'sAdvancedAnalyticsteamhasdevelopedararediseasepredictionmodelthatlearnsthedataandsymptomcharacteristicsofconfirmedrarediseasecases,assistingdoctorstoclassifyanddiagnoserarediseasesaccuratelyandefficientlytoeliminateexpensive,complexandtime-consuminglaboratorytests,inadditiontoreducingrelianceondoctors'experienceinrarediseasediagnosisprocess.Therarediseasepredictionmodelcancovermillionsofpeople,outputthediseaseprobabilityscoreofrarediseasesbasedoneachatientsmedicalrecorddataidentifypotentialpatientswithrarediseasesabovethethresholdvalueandreducethemisseddiagnosisandmisdiagnosisofrarediseasesthusimprovingtheaccuracyandefficiencyofrarediseasediagnosis.Therarediseasescreeningmodelbasedongenerativeadversarialnetwork(GAN)andlongshorttermmemorynetwork(LSTM)canlearnthecharacteristicsofconfirmedpatientsfromalargenumberofpatientdataandaccuratelyidentifyindividualpatientswithpotentialdiseaseriskunderthepremisethattheexistingconfirmedcasesaresparsethusprovidingaccurateandtargeteddecisionassistanceforrarediseasepreventionanddiagnosis.Figure2.1)FigureGenerativeAdversarialNetworkSimulatesIdentificationofPotentialPatientsPatientPatientPopulationatrisknalpatientdataDiagnosis1Diagnosis2Diagnosis3PrescriptionInjectionSurgeryPatient1Diagnosis1SurgeryPrescriptionntPatientKMachineMachinelearningandDeepLearningalgorithmicmodeldevelopedtoesparsepositivelabelproblem““TheJudge”natorainingsampleGBothgetsmarterwithtrainingAwinninggeneratormeansthemodelhaslearnedhowtogenerate“near-perfectartificialdata”ator“TheFooler”eDourceIQVIAinternalmaterialsCaseStudyOmnichannelmarketingintelligentrecommendationsystemticalandmedicaldeviceenterprisesandhealthcareprofessionalsreliesontheexperienceofmedicalrepresentatives.Itisdifficulttoachieveacomprehensivereachontreatmentconceptsanddiseaseviews.AIMLanalysistoolscanhelppharmaceuticalandmedicaldeviceenterprisesoptimisethecommunicationchannelstoprovidemoreaccurateandcomprehensivetreatmentinformationandsolutionsByintegratinginternaldataandpublicdata,AI/MLalgorithmscanestionstobettertakeadvantageofomnichannelmarketinganddeliveraccuratedrug(Figure2.2.2).igureOmniChannelIntelligentRecSysImprovesPrecisionFeature1Feature2FeatureNurceIQVIAinternalmaterialsXRec.SystemRec.SystemEasyScalabilityAI/MLTechnologyProvidesSolutionScalabilityFacedwithacomplexandchangingmarket,pharmaceuticalenterprisesneedtofrequentlymakereal-timeandpreciseadjustmentstobusinessdecisionsinresponsetomarketevents.Traditionalbusinessanalysisdecisionsoftenrequirerepeatedcallstohistoricalexperiencetocompleteneedsandachievingflexibleexpansionofalgorithms,dataandmodels.aseStudySalesforecastingplatformWithregularizedVBPmoreenterprisesneedtofrequentlyforecastbasedonthelatesttrendofcentralisedvolumeprocurement.ConsideringthecontinuousexpansionofthecategoriesofVBP,itisnormalforenterprisestodothiskindofforecastingandmakecommercialpromotionstrategiesformultipleproducts.Traditionalbusinessforecastingprocessesrequireindividualdatacollection,modelbuildingparameteradjustmentandoutputforecastingforeachproductaffectedbysimilarevents,withhighhumanresourceconsumptionandlongprojectcyclesWiththeassistanceofAI/MLtechnologiesenterprisescanuseauser-friendlyplatformthatintegratesAIMLpretrainedmodels,combinetheirownuploadeddataandmarketdatawithintheplatform,definethenature,impact,andtimewindowofimportantmarketevents,andflexiblycallalgorithmicmodelsbasedonstatisticalmethodstoforecastsalesaffectedbytheeventsThesalesforecastingplatformiseasilyscalableandgreatlyenhancestheefficiencyofmarketdecisionmaking.(Figure2.3.1)gureSalesforecastingPlatformEnhancesDecisionEfficiencyntVBPimpactsimulationBaselinemodelresultMonteCarloparameterEventsimulationresultourceIQVIAinternalmaterialsacomCaseStudyAdverseDrugeventalertplatformTraditionalpharmacovigilancepracticesrelyonmanualcollectionofcollectionofcaseinformationAI/MLtechnologycaneffectivelyhelpenterprisesovercomethebottlenecksinpharmacovigilance.TheadverseeventalertmodelbasedonBERT,LSTM,RNNandotheralgorithmscanautomaticallydetermineadversedrugeventsbasedondatasampleslabelledbyenterprisesthroughpre-setkeywordsandlearningstructuredtextformedbyin-depthinterviewswithenterprisesTheadversedrugeventalertplatformhasthreemajorigentpromptinganchorpointsTheplatformallowsuserstoadd,delete,andmodifyadverseeventelementsbythemselves,andmakeappropriateadjustmentstothemodelaccordingtospecificbusinesses.Theplatformcanalsoachieveself-learninginthepharmacovigilancetaskthroughdataaccumulationtoachieveself-learning,usinguploadeddocumentstoformstructureddataassetsfornaturallanguageprocessingmodeliterationTheplatformintegratingAI/MLeralizabilityofthenmentthemodelwasabletoreducetheaveragedocumentreviewtimeforskilledusersbyuptowithnomissedreports(100%recallrate).(Figure2.3.2)FigureAdverseDrugEventAlertPlatformImprovesScalabilityofPharmacovigilanceModelDocumentProcessingIDIdocument=>Structuredtextsonfirmresultsfromdigitalco-workerNLPmodel+keywordsearchourceIQVIAinternalmaterialsReportAutomationforfurtherreportprocessingDeepInsights:AI/MLTechnologyEmpowersScientificDecisionThecontinueddevelopmentofChina'shealthcaremarkethascreatedabundantsourcesofvarieddata.AI/MLtechnologycanhelpenterprisestobetterutilizeandinterprettherichmarketdatainunprecedentedways.UsingMLandstatisticalmodelsenterprisescancollect,analyze,andandrealizealgorithm-drivenbusinessdecisions.eStudySociallisteningWithavastmarketandcomplexindustrialstructure,healthcareenterprisesneedtokeepaneyeontheopinionsandsuggestionsfrommultiplestakeholderssuchasdoctors,patients,caregivers,medicalinstitutionspharmaceuticalenterprisesandgovernment.Withthedevelopmentofmobileinternet,pharmaceuticalandmedicaldeviceenterprisesareabletocollectopinionsandinformationfrommultiplepartiescomprehensivelyandconvenientlythroughonlineinternetchannels,providingst