數(shù)據(jù)挖掘數(shù)據(jù)整合及知識(shí)管理在健康信息學(xué)的應(yīng)用

數(shù)據(jù)挖掘數(shù)據(jù)整合及知識(shí)管理在健康信息學(xué)的應(yīng)用第1頁(yè)/共102頁(yè)公共資金投入美國(guó)2006澳洲2006-2009中國(guó)2003-2007$7830.8$460.3$2410￥567￥103015.7%翻番第2頁(yè)/共102頁(yè)發(fā)展電子健康（健康信息學(xué)）的建議／薦言計(jì)算機(jī)，數(shù)據(jù)庫(kù)技術(shù)和現(xiàn)代通信技術(shù)發(fā)展為衛(wèi)生醫(yī)學(xué)數(shù)據(jù)集成和利用提供了可能高危疾?。ㄈ鏑ancer）的早期發(fā)現(xiàn)和診治突發(fā)公共衛(wèi)生事件（如SARS和H1N1）的預(yù)測(cè)和監(jiān)測(cè)新一代Web技術(shù)（Web2.0）衍生出大量社會(huì)媒體第3頁(yè)/共102頁(yè)背景和意義(2/2)電子健康（e-Health），健康（醫(yī)療）和生命信息技術(shù)（HealthInformatics）電子病歷手持式設(shè)備數(shù)字式醫(yī)療儀器醫(yī)學(xué)數(shù)據(jù)分析……數(shù)據(jù)集成與管理第4頁(yè)/共102頁(yè)醫(yī)學(xué)衛(wèi)生數(shù)據(jù)管理挑戰(zhàn)×1.信息“孤島”，缺乏統(tǒng)一標(biāo)準(zhǔn)和數(shù)據(jù)交換性2.海量數(shù)據(jù)沒有得到充分利用第5頁(yè)/共102頁(yè)建議(61stNationalDayPresentation)

成立專門的電子健康研究，開發(fā)及推廣機(jī)構(gòu)協(xié)助參與國(guó)家有關(guān)衛(wèi)生管理和標(biāo)準(zhǔn)化制定部門開展電子健康和病人數(shù)據(jù)管理的國(guó)家標(biāo)準(zhǔn)探討衛(wèi)生數(shù)據(jù)交換和服務(wù)相互可操作性開發(fā)個(gè)人與病人數(shù)據(jù)管理技術(shù)醫(yī)院和衛(wèi)生機(jī)構(gòu)組織保存電子數(shù)據(jù)的立法管理隱私保護(hù)和數(shù)據(jù)保護(hù)技術(shù)基于慢性病預(yù)防和治療的公共衛(wèi)生知識(shí)的門戶網(wǎng)站建立和推廣衛(wèi)生信息化人才的教育和培養(yǎng)工作流行病和突發(fā)衛(wèi)生事件的檢測(cè)和預(yù)報(bào)體系第6頁(yè)/共102頁(yè)主要涵蓋內(nèi)容研究如何從公共管理和政府職能層面上推動(dòng)、建設(shè)和逐步完善電子健康和醫(yī)學(xué)信息化所涉及的公共體系結(jié)構(gòu)相關(guān)數(shù)據(jù)標(biāo)準(zhǔn)和信息交換標(biāo)準(zhǔn)相關(guān)法律法規(guī)的制定衛(wèi)生信息化人才培養(yǎng)等宏觀管理內(nèi)容如何建立一個(gè)基于互聯(lián)網(wǎng)技術(shù)的數(shù)據(jù)集成和共享平臺(tái)（基礎(chǔ)設(shè)施）并探索現(xiàn)代數(shù)據(jù)挖掘和智能信息處理在公共健康信息學(xué)中的技術(shù)應(yīng)用研究第7頁(yè)/共102頁(yè)84/11/2023CentreforAppliedInformatics

(CAI)

@VictoriaUniversityCentreforAppliedInformatics,VictoriaUniversity,startedase-ResearchGroupfrom2005.e-ResearchisICT-enabledapplicationdrivenandmultidisciplinaryresearch;Theteamhas15academics,5postdoctoralfellows,20+postgraduatestudentsacrossthreeFacultiesinVU;e-Researchprojectsinclude:e-Health,e-Environment,e-Business,e-Education,e-Community,e-Securityetc.第8頁(yè)/共102頁(yè)Innovativee-Research第9頁(yè)/共102頁(yè)ARCProjectsGrantedARCDiscoveryProjects:

X.YiandY.Zhang,PrivacyProtectioninDistributedDataMiningY.Zhangandetc.,AFrameworkforSupportingConsistentandReliableCollaborativeBusinessTransactionsARCLinkageProject:Y.Zhang,X.Yi,J.He,M.Steyn,J.Cao,KesiT.,Real-timeandSelf-AdaptiveStreamDataAnalyserforIntensiveCareManagement,ARCLinkageProject(LP100200682),2010-2013($345,000)Y.MiaoandY.Zhang,DataExchangeandServiceIntegrationwithApplicationsinHealthInformationSystems,ARCLinkageProject(LP100100624),2010-2012

($312,453)X.ZhouandY.Zhang,DataEnhancement,IntegrationandAccessServicesforSmarter,CollaborativeandAdaptiveWhole-ofWaterCycleManagement(2008-2010)ARCe-ResearchProject:Y.Zhangandetc.,Peer-to-Peercollaborativeresearchnetworkforsharingandmanagingdigitallegalinformation(2005-2006)第10頁(yè)/共102頁(yè)FundamentalResearch&ProjectsWebdatamanagementWebSearchandWebCommunityWebDataMiningandWebRecommendationWebDatabases&WebDataIntegrationSemanticWebWebservicesServicecomputingCollaborativebusinesstransactionmanagementPrivacyprotectionindistributeddataminingE-Commerce&SecurityAccessControlManagementCloudcomputingDataprocessingandDataMiningSpatialtemporaldataminingOptimizationbaseddataminingReal-timestreamdatamining第11頁(yè)/共102頁(yè)MonographsWebCommunities:Analysis,ConstructionYanchunZhang,JeffreyXuYu,JingyuHou

SpringerBerlinHeidelbergNewYork2006

ISBN3-540-27737-4WebMiningandSocialNetworking:TechniquesandApplicationsG.Xu,Y.Zhang,L.Li,

ISBN978-1-4419-7734-2,

Springer,2010.

第12頁(yè)/共102頁(yè)Application-drivenresearche-Healthe-Environmente-Businesse-EducationCommunitySecurityetc2023/4/1113第13頁(yè)/共102頁(yè)GUCAS-VUJointResearchLabin

SocialComputingandE-Health第14頁(yè)/共102頁(yè)15第15頁(yè)/共102頁(yè)16InternationalJournalofHealthInformationScienceandSystems,BioMedCentral.

InternationalConferenceonHealthInformationScience(HIS’2012),Beijing,April8-10,2012

APWeb’2012,Kunming,April11-14,2012ProfessionalActivities第16頁(yè)/共102頁(yè)e-HealthProjectsEPiSurvey(WHO)GPe-ConnectReferral(ARCLinkage,WestgateGeneralPracticeNetwork)WebEPiGoogleMapping(TasmaniaGovernment)GaitAnalysisUsingClustering（AustralianRehabilitationCentre）Real-timeandself-adaptivestreamdataanalyserforintensivecaremanagement(ARCLinkage,BrisbaneRoyalandWomen’sHospital)Socialnetworkingfordiabetics(AustralianCommunityCentreforDisbetes)2023/4/1117第17頁(yè)/共102頁(yè)1.TheFrameworkofHealthdata&KnowledgeManagementHealthDataacquisition&RepositoryElectronichealthrecordsElectronicdatainterchangeDatamanagementHealthinformation/serviceintegrationDecisionsupportHealthdata&KnowledgeManagement…………第18頁(yè)/共102頁(yè)19WorkflowHealthdatawarehouseanddataunionKnowledgeDiscoveryMobileEquipmentLocalEquipmentDataStorageDataGatheringDataFusionDataMiningHealthSocialNetworkWirelessSensorNetworksWebmining&ServiceDataSecurityPatients

GPs/Doctors

HospitalsLocalDatasetsDatabasePublicDatabaseContext-awareLogicStructureInternet第19頁(yè)/共102頁(yè)

FederatedDataIntegratorVPNGenBankUniProtPubMedLocusLinkPublicDataSourcesMetadataRepository

UniqueSubjectIndexETLhosp3LRRETLhosp1LRRVPNDe-identifiedLinkeddataETLhosp2LRR…………第20頁(yè)/共102頁(yè)前期電子健康工作積累世界衛(wèi)生組織合作（WorldHealthOrganization，WHO）

--流行／慢性病信息獲取，流行病數(shù)據(jù)管理系統(tǒng)(EpiSurvey)Melbourne衛(wèi)生機(jī)構(gòu)（WestgateGeneralPracticeNetwork）--各醫(yī)院間病人信息連接和傳送系統(tǒng)（GPe-ConnectReferral）

Tasmania衛(wèi)生廳--基于Web的流行病數(shù)據(jù)管理及Google地圖顯示澳大利亞康復(fù)中心（Victoria)--基于聚類的步態(tài)分析（GaitAnalysisUsingClustering）Brisbane皇家及婦幼醫(yī)院--重癥護(hù)理實(shí)時(shí)和自適應(yīng)的流數(shù)據(jù)分析器（Real-timeandself-adaptivestreamdataanalyserforintensivecaremanagement)澳大利亞社區(qū)唐尿病研究中心--在線糖尿病人社區(qū)網(wǎng)絡(luò)Socialnetworkingfordiabetics第21頁(yè)/共102頁(yè)22ChallengesChallenge1:heterogeneousdataexchangeanddataservicesinmultiplelevels

MassiveDataStorage

MassiveDataTravelandIndex

MassiveDataMining:Uncertainty,UnreliabilityandlowefficientalgorithmsChallenge2:openproblemtodevelopdataminingmethodsformassive,complexhealthdata.

Inadequatehistoricaldata,Standarddatainterface,Privacy,Security,Costs,LackofexpertsChallenge3:DomainExpertise第22頁(yè)/共102頁(yè)2.PreviousWorkandState-of-ArtResearch

皇家布里斯本和婦女醫(yī)院世界衛(wèi)生組織澳大利亞國(guó)立研究院澳大利亞電子健康研究中心墨爾本西部醫(yī)生聯(lián)盟澳大利亞塔斯馬尼亞市政府…23第23頁(yè)/共102頁(yè)OurPreviousWorkinE-Health世界衛(wèi)生組織合作（WorldHealthOrganization，WHO）

--流行／慢性病信息獲取，流行病數(shù)據(jù)管理系統(tǒng)(EpiSurvey)Melbourne衛(wèi)生機(jī)構(gòu)（WestgateGeneralPracticeNetwork）--各醫(yī)院間病人信息連接和傳送系統(tǒng)（GPe-ConnectReferral）

Tasmania衛(wèi)生廳--基于Web的流行病數(shù)據(jù)管理及Google地圖顯示澳大利亞康復(fù)中心（Victoria)--基于聚類的步態(tài)分析（GaitAnalysisUsingClustering）Brisbane皇家及婦幼醫(yī)院--重癥護(hù)理實(shí)時(shí)和自適應(yīng)的流數(shù)據(jù)分析器（Real-timeandself-adaptivestreamdataanalyserforintensivecaremanagement)澳大利亞社區(qū)唐尿病研究中心--在線糖尿病人社區(qū)網(wǎng)絡(luò)Socialnetworkingfordiabetics第24頁(yè)/共102頁(yè)HeterogeneousDataFusion25第25頁(yè)/共102頁(yè)

FederatedDataIntegratorVPNGenBankUniProtPubMedLocusLinkPublicDataSourcesMetadataRepository

UniqueSubjectIndexETLhosp3LRRETLhosp1LRRVPNDe-identifiedLinkeddataETLhosp2LRR…………第26頁(yè)/共102頁(yè)

2.1AnInnovativeModelforMedicalServiceIntegration

SignificantLinkedMedicationErrors2%-3%ofhospitaladmissionsinAustraliahavelinkedmedicationerrorsitequatesto190,000admissionseachyearcoststhehealthsystem$660millions

--MinisterforHealthandAgeing,11May2010MedicalDataExchange:AGlobalChallengeIntheUnitedStates,about25%ofemergencydepartmentpatientshadmedicalinformationstoredinanothermedicalsystem.第27頁(yè)/共102頁(yè)MedicalServiceIntegration

DataExchange:GPeConnectEvidencebasedResourceAllocationNewServicesKnowledge/DataMiningDecisionSupportServiceDiscoveryServiceIntegrationWorkFlowServiceDirectoryDataIntegrationDataExchangeSchemaMappingTrackingEMRGPEMRHospitalEMRSpecialistEMRPharmacyEMRLaboratory第28頁(yè)/共102頁(yè)DataExchange:GPeConnectEvidencebasedResourceAllocationNewServicesKnowledge/DataMiningDecisionSupportServiceDiscoveryServiceIntegrationWorkFlowServiceDirectoryDataIntegrationDataExchangeSchemaMappingTrackingEMRGPEMRHospitalEMRSpecialistEMRPharmacyEMRLaborotaryClick1:StartreferringClick2:SelectreceiverClick3:Sendthereferral第29頁(yè)/共102頁(yè)DataExchange:GPeConnectEvidencebasedResourceAllocationNewServicesKnowledge/DataMiningDecisionSupportServiceDiscoveryServiceIntegrationWorkFlowServiceDirectoryDataIntegrationDataExchangeSchemaMappingTrackingEMRGPEMRHospitalEMRSpecialistEMRPharmacyEMRLaborotaryGPeConnectusesstandardHL7fordataexchange,orRSVFdefinedlocallyinAustralia,orcustomerdefinedRTF,PDF.TheexchangedataisaXMLwhichenablethetargetsystemtochoosetheformat.第30頁(yè)/共102頁(yè)ServiceIntegration:GPeConnectEvidencebasedResourceAllocationNewServicesKnowledge/DataMiningDecisionSupportServiceDiscoveryServiceIntegrationWorkFlowServiceDirectoryDataIntegrationDataExchangeSchemaMappingTrackingEMRGPEMRHospitalEMRSpecialistEMRPharmacyEMRLaborotaryOrganisationalReferralManagementDiversionReferraltracking&Instantcommunications第31頁(yè)/共102頁(yè)WorkFlowandCDM:GPeConnectEvidencebasedResourceAllocationNewServicesKnowledge/DataMiningDecisionSupportServiceDiscoveryServiceIntegrationWorkFlowServiceDirectoryDataIntegrationDataExchangeSchemaMappingTrackingEMRGPEMRHospitalEMRSpecialistEMRPharmacyEMRLaborotaryTeamCareCreateCarePlanbysimpleclicks第32頁(yè)/共102頁(yè)2.2ClusterAnalysisofGaitPatternsforDetectingRiskofFalling

Issue:Australiaisfacinganageingpopulation.Fallsandrelatedinjuriesintheelderlyisamajorpublichealthissue(costs~$2.4billionpatoAustralia).Solution:Gaitorwalkingpatternanalysiscandetectabnormalitiesandevaluatewalkingperformance.Clusteranalysisofgaitfeatureswillbeusedtoidentifypotentialfallers第33頁(yè)/共102頁(yè)AMTIPitch-RollTreadmillallowsevaluationofwalkingandrunningkinetics,simulatingarangeofsurfacecharacteristics(tiltandinclinationangles±140).第34頁(yè)/共102頁(yè)ClusteringanalysisresultexampleFig.1ClustervisualizationforgaitdataofChildrenwithCerebralPalsyFig.2Gaitpatterndistributionforonepatientatthreeagesofgaitrecovery(Cluster1=normalgaitcluster,Cluster5=severepathologicalgaitcluster)第35頁(yè)/共102頁(yè)AgingTrendinChinesePopulation

[2]YearPopulation（billion）Populationwith60yearsoldPopulationover65Population（billion）Percentage（%）Population（billion）Percentage（%）200012.71.3110.340.917.13201013.761.7312.541.158.38202014.722.4516.611.7411.83203015.243.5523.32.4415.98204015.434.0926.523.2420.98Table1AgingTrendinChina[1]注：[1]耿忠平.社會(huì)保障學(xué)導(dǎo)引.同濟(jì)大學(xué)出版社,2003年6月。InternationalconferenceaboutagingproblemheldinVienna

defines“Aging-Type”ContriesorArea,ifoneofthecriteriaissatisfied:Populationover60isover10%oftotalpopulation;Populationover65isover7%oftotalpopulation.“Aged-Society"isdefinedas:Populationover65isover14%oftotalpopulation.“ExtraAgedSociety”isdefinedas:Populationover65isover20%oftotalpopulation.Conclusion:Chinaisa“Aging-Type”Contrysince2000;willbea“Aged-Society”since2030;andwillbea“ExtraAgedSociety”since2040.[2]周興社，於志文，“面向老年人生活的智能輔助”，中國(guó)計(jì)算機(jī)學(xué)會(huì)通訊6（6），2010年6月第36頁(yè)/共102頁(yè)InternationalAgingTrendCountryYearover60over65Population（Billion）Percentage（%）Population(Billion）Percentage（%）China2000(2040)10.34%7.13%(20.98%)Japan200520%Europe2002(2050)20%(30%)20%(2007)U.S.A2000(2030)12.4%(19.7%)Table2StatisticResultsfromEconomyandSocialaffaircommitteeofU.N.in2008Note:Purple:”Aging-Type”CountryorArea;Blue:”Aged-Society”;Green:“ExtraAged-Society”.Conclusion:（1）ChinaisthesameasUSA:beingaAging-TypeCountrysince2000.USAwillbeaseriousAged-Societysince2030andneartoaExtraAgedSociety.（2）JapanbecomeaExtraAgedSocietysince2005.ChinawillbeaExtraAgedsocietyin2040.（3）EuropebecomeatypicalAging-TypeAreasince2002.WededucedthatithasbecomeaExtraAgedSocietysince2007.Deducedresults第37頁(yè)/共102頁(yè)2.3Real-timeStreamDataMiningforIntensiveCareManagementExampleofapplication:Abnormalitydetectionandpredictionforanesthetist’soperating第38頁(yè)/共102頁(yè)Motivation-DataScenario

VariousDevicesareusedtomonitorthepatient'sbloodpressure,heartrate,temperature,andothervitalsigns.Thesedata-arecriticalinformationforthesurgeonand/ormedicaltreatmentteam.第39頁(yè)/共102頁(yè)Motivation–lackofefficientalgorithm/system(i)thelackofefficientandeasilyusedonlinealgorithmstopredictpatientdeterioration,(ii)thevolumeofdataistoolargetobeeffectivelymanaged,processedandstoredinexistingdatabases.第40頁(yè)/共102頁(yè)Example:CoarsesampleddataandlowreusabilityinRBWH

Asmanyphysiologicaldatacollectedduringsurgeryare

highfrequencydata(about200-500Hz),theexistingsystemscanonlystoreacoarsesampleofthedatawithalongintervalduetostoragelimitations.Forexample,theAnaesthesiaDatabaseoftheRoyalBrisbaneandWomen'sHospital(RBWH)storesonedatavalueperminuteforeachphysiologicaldatastream,includingECGdata.NeithertheHPnorVAquantitativesentinelsystemscananalysethesedatanordetectvariationsinrealtime.第41頁(yè)/共102頁(yè)Motivation–Existingsystem

Monitoringintensivecarepatientsrequirestheanalysisofatremendousamountofstreamingdatacollectedfrommanysensorsandinatimelyfashion.Existingapproachesareinefficientandnotfullyautomatic.Thisprojectdevelopsoriginalalgorithmicandmodelsolutionforonlinestreamingphysiologicaldataprocessing.Forexample,inanRBWHdataset,fivepatientswerelistedashavingimpendinganaphylaxisbecauseofimpropertrendsvariation,whereasinfact,onlyonepatientshouldhavebeenclassifiedinthisway.第42頁(yè)/共102頁(yè)ThreeSchemesPreOperating:Personaldataintegration,Expertknowledgereviewforbothoperatingandinstrument/medicinepreparation.(Aim1)1.1Personaldata:Personalmedicaldataloading,enhancement,integrationandaccessservices1.2Operatingreview1.3Instrument/medicinereviewOperatingtheater:Improvedearly-warningfunctionbasedontrendvariation(Aim2).2.1Abnormalitydetection,2.2trendprediction,2.3expertknowledgePostOperating:Improvedintensive-care-cyclemanagementbasedonquantitativeanalysisofpatientdatastreams(Aim3).3.1Offlineintensivecarecyclemanagement第43頁(yè)/共102頁(yè)P(yáng)ersonaldataNameofPatientHeightEmergencyreportMedicareIDweightFulldatareportDateofBirthstateofillnessConsultantHistoryAgePresentMedicalconditionsConsultantReferralsGenderAllergyConsultantSummaryMarriagestatusMedicineHistoryFinicalAccountInformationAddressImmunizationsAddressOperationPartsMedicalImagesstorageContactDatasources:Admission,GP,emergency,Medicare/Medibank,operatingtheatre,ICU,WardDatabaseandotherpersonalinput第44頁(yè)/共102頁(yè)1.2OperatingreviewSurgeon/

anaesthetistcanretrievethesimilaroperatingrecordfromhistoricalcasestudy.Patient’sphysicalconditionwillbereviewedbeforeoperation.(e.g.medicineintake,diet,smoking)

1.3Instrument/medicinereview(animationplayer)Thesimulatedoperatingprocesswithallinstrumentsandmedicineswhichwillbeusedduringtheoperatingwillbegothrough.第45頁(yè)/共102頁(yè)TheoreticalFoundation

-IntelligentHealthmonitoratoperatingtheatreSlidingwindowstovisualizestreamphysiologicaldata3DdemonstrationforsensordeploymentandoperatingsiteReal-timeDensity-BasedClusteringofApplicationswithNoise(DBSCAN)algorithmforclusteringanddetectingabnormalBack-PropagationNeuralNetworks(BPNN)algorithmforpredictiontrend第46頁(yè)/共102頁(yè)RealTimeandIntelligentPhysiologicalDataMonitorSecurityloginThreeschemesPatientInfoloadingSystemsettingsSlidingwindows3DDisplaySensorsandoperatingsiteAbnormalDetection1.ExpertKnowledge2.AutomaticallystreamdataminingTrendPrediction第47頁(yè)/共102頁(yè)P(yáng)atientInformationloadingSeveralkeyparameters:AgeWeightIllnessForAbnormaldetectionOperationRegionFor3Ddisplaysensors第48頁(yè)/共102頁(yè)AbnormalDetectionExpertKnowledgeDataMiningandAnalysisOurnovelAlgorithm:ExceptionalObjectAnalysisPatternMatch20%:80%Fromothersimilarpatientsandhistoricaldataofcurrentpatient第49頁(yè)/共102頁(yè)ExceptionalObjectAnalysisDBSCANclusteringAlgorithmComplexityMin:O(n)Max:O(nlnn)第50頁(yè)/共102頁(yè)AlgorithmComplexityO(n)Realtime2000points<30ms第51頁(yè)/共102頁(yè)Fig.2.ExampleResult第52頁(yè)/共102頁(yè)RealTimeMonitor第53頁(yè)/共102頁(yè)TrendPredictionAheadofTime:10minutesPatternMatch80%FromothersimilarpatientsandhistoricaldataofcurrentpatientPredictionBelievableDegreePatternmatch:historicalvalue68%:32%第54頁(yè)/共102頁(yè)RealTimeMonitorSystemforPredictingAbnormalPhysicalCurveChanges55第55頁(yè)/共102頁(yè)IntelligentAnalysisCentreAbnormal?Y56第56頁(yè)/共102頁(yè)P(yáng)ost-operating:Improvedintensive-care-cyclemanagementbasedonquantitativeanalysisofpatientdatastreamsOfflinesystem:integratedalldatafromdifferentdatasourceMiningfrequentpatternsAnalyzingsimilaritiesamongpatientsPeriodicpatternssuchas“Patientswhoarelowinoneattributearelikelytobehighincertainotherattributevaluesin48hours”

whichrecurinregularperiodsordurationsarethemainpatternswithinintensive-care-cyclemanagement.Commonqueriessuchas“Howmanypatientshaveanaphylaxisorhypovolemia?”canbeimportantforclinicalevaluationandmanagement.Thisstudycanbesignificant.第57頁(yè)/共102頁(yè)NationalBenefit

improvedsuccessratesandreducedmortalityandriskinsurgeryandintensivecareunits.Theproposedstreamdataanalyserwillreducetheclinician’sworkloadandenhancetheirmedicalperformancewithoutcompromisingtheservicequality.Tothebestknowledgeofthedevelopers,thisreal-timestreamdataminingsystemisthefirstsystemtoanalysisphysiologicalstreamdata.第58頁(yè)/共102頁(yè)FutureDirection

Remotemonitor(PortableECG+wirelesssensornetwork+PDA+Webservice)LowcostHighspeed(combinedwithWaveletcompression)HighaccuracyMoreintelligent(Voiceagent,translator)第59頁(yè)/共102頁(yè)2.5GeneDataMining:MeasurementofGeneMutation3DCoordinator,Spacechange（DiscreteProteinChain），Sequencechange，MeasurementbasedonEurodistance，similarityanalysisExtractDNAsequencesfromSaliva，Blood,Skincell，cellsininternalbody.Giventwoproteinsequences:SandS’，WecanuseimprovedBasicLocalAlignmentSearchTool(BLAST)

algorithmtoautomaticallycomputesynthesis

mutationscoretomeasuregenechange.CompareabnormalDNAsequencesinTypeIIdiabeteswithDNAsequencesinnormaltissuetodistinguishthediseasefactor.60第60頁(yè)/共102頁(yè)2.6SystemforMergingGeneMutationandPhysicalChangetoPredictandDiagnoseTypeIIDiabetesFig.2:DiscoverDiseaseFactorofTypeIIDiabetesbyMergingGeneMutationandPhysiologicalChangebasedon3DCubeLearningfromsimilarorsamepatientsbyanalysisofDNAandPhysicalcurves.61PhysicalChange,GeneMutationTypeIIDiabetesTypeIIDiabetes,PhysicalChangePhysicalChange,GeneMutation,TypeIIDiabetesGeneMutationPhysioChangeTypeIIDiabetes,GeneMutation第61頁(yè)/共102頁(yè)3.SocialNetworkingandApplicationsinHealthInformatics第62頁(yè)/共102頁(yè)Web1.0vs.Web2.0KDD08BlogosphereTutorial.pdf第63頁(yè)/共102頁(yè)P(yáng)agetoPeopleSocialWebWebforPeople’sSocialInteractionandCommunity

第64頁(yè)/共102頁(yè)第65頁(yè)/共102頁(yè)EntertainmentAdvertisingInformationLearningShoppingSearchSocialWebAsAPlatformMediaBusinessMarketingPoliticsGovernment第66頁(yè)/共102頁(yè)GrowthTimeInformationandknowledgeHumanabsorptivecapacitySocialFilterCohen,WMochLevinthal,DA,AbsorptiveCapacity:AnewPerspectiveonLearningandInnovation,Workingpaper,CarnegieMellonUniversityandUniversityofPennsylvania,October1989第67頁(yè)/共102頁(yè)AdsonmobilephonesWebbanneradsSearchengineadsBrandedWebsitesAdsinmagazinesAdsonradioAdsonTVAdsinnewspapersRequestedemailupdatesConsumeropinionspostedonline0%20%40%60%80%100%Recommendationsfromfriends/familyTrustBasedFilter

WisdomofCrowd,Friendsourcing/2007/05/31/the-new-portals-its-the-bread-not-the-peanut-butter第68頁(yè)/共102頁(yè)SocialMediaWebApplicationsandServicesforSocialInteraction第69頁(yè)/共102頁(yè)

SocialWebPlatforms

FacebookTwitter第70頁(yè)/共102頁(yè)CharacteristicsofSocialWebUseristhedrivingroleofcurrentWebAutonomyofWebUserGeneratedContent(UGC)CollaborativeandSocialenvironmentDynamicalandevolutionalReflectiveandrepresentative…第71頁(yè)/共102頁(yè)OpportunitiescreatedbySocialWebLargerdatavolumeCollectivemindHiddenknowledgeunseenbytraditionalapproaches第72頁(yè)/共102頁(yè)ExponentialGrowingofDataMoreDataOpenSensorGeneratedUserGenerated第73頁(yè)/共102頁(yè)

CollectiveMind第74頁(yè)/共102頁(yè)EmergingresearchissuesinSocialWebMiningContentMiningSentimentalanalysisandOpinionminingEventdetectionthroughidentificationofbreakingnewsClusteringBlog/MicroBlogSocialNetworkAnalysisUnderstandingofstructureandpropertiesofblogosphereCommunitydetectionfromsocialmediaStudyofinformationdiffusion第75頁(yè)/共102頁(yè)EmergingresearchissuesinSocialWebMining(cont.)InfluenceanalysisInfluencerankingmethodsCredibilityidentificationAnti-spammingFightingrumoursofbreakingnewsTemporalmonitoringIdentificationofburstyevents(breakingnews)MonitoringtheuserattitudepulsetowardseventsorproductsTrendanalysisthrougheventdetection第76頁(yè)/共102頁(yè)SearchinSocialWebBlog/Microblog/TwittersearchLocatingpeople/blogsbyname/themeTemporalnatureNewdimensioninsearchwithSocialWebInformationoverload–searchengineslimitedcapabilitySmartsearch–personalizedsearch,context-awaresearch…第77頁(yè)/共102頁(yè)SocialsearchThedifferenceinmethodology–from“l(fā)ibrary”ofsearchto“village”ofsearchTrustinWebsearchbasedon“authority”TrustinSocialsearchbasedon“intimacy”MoreadvanceinrecommendationFriendsourcingrecommendationTrust-basedrecommendation第78頁(yè)/共102頁(yè)

Application1:Intelligentpubliceventdetection

OneundergoingworkofSocialWebMining第79頁(yè)/共102頁(yè)EventTerminologyusedinmediaandnewsanalysisEventisdefinedastheaperiodicorperiodicburstofobservedfeatures,whichcouldbenames,locations,phenomenaandsoonexampleaperiodiceventofterm“Christmas”periodiceventofterm“soccer”(Heet.Al.SIGIR2007)第80頁(yè)/共102頁(yè)P(yáng)ublicHealthEvent(PHE)Inthecontextofpublichealth,theeventisdefinedastheburstyobservationoffeatures,suchasvictims,locations,symptomsandtimesEvent-basedepidemicintelligenceistodetectthePHEinepidemiology第81頁(yè)/共102頁(yè)IntelligenteventdetectionapproachesEventdetectionisapartofbroadinitiativesofTopicDetectionandTracking(TDT)maingoalistolookthroughthecollectionofdocuments,andanswer“whathappened”(retrospective)and“whatisNew”(newevent)Evolutionofmethodologiesearlyongranulardocument-based(e.g.AllanSIGIR’98andYangSIGIR’98)–usingcontentandtemporalinfotoconductdocumentclusteringrecentlyonfinerfeature-based–moremachineintelligenceapproaches(e.g.HeSIGIR’07andLiSIGIR’05)第82頁(yè)/共102頁(yè)AProbabilisticModelforHealthEventDetectionBasicidea:Datasource:crawledsocialmediadata,contentanalysis–namedentitiesEvent:ahiddenvariable(liketopics)estimatedfromtheobserveddataMethod:usingthegenerative(topic)modeltoestimatetheprobabilitydistributionofeacharticleontheeventspace第83頁(yè)/共102頁(yè)ApproachstepsContentProcessing-Namedentityfeaturerepresentationarticle={victims,diseases,locations,time}Forexampleonearticlexixi={victimcsi,diseasesi,locationsi,timei}victimcsi=<victimcsi1,victimcsi2,…,victimcsiN>andsimilarprocessingondiseases,locationsandtimes…Formulatethegenerative(Topic)ModelModelTraining(MachineLearning)Labelthearticles(KnowledgeUtilization)第84頁(yè)/共102頁(yè)85Application2:BuildingaheterogeneousIntelligentMedicalSocialNetworkforEpidemiologyAnalysis第85頁(yè)/共102頁(yè)86ResearchObjectivesExplorethesocialnetworkswithpatientsandgeographicallocationsforcomplexdiseaseinvestigation(e.g.Asthma)ExaminethepossibilityofusingSNAwithgeographicalnodesforbetterunderstandingthedynamicofT2DMdevelopment第86頁(yè)/共102頁(yè)87SocialNetworkAnalysisinEpidemiologyNetworkConstructionAhe