new a. holzinger 709.049 mi, 04.11 - human-centered.ai · 2015. 11. 4. · biomedical data...
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Status asofDi,03.11.2015,10:00
Dear Students,welcometothe4thlectureofourcourse.Pleaserememberfromthelastlecture:modelingofknowledge,medicalOntologies,ClassificationeffortsandtheInternationalClassificationofDiseases(ICD);StandardizedNomenclatureofMedicineClinicalTerms(SNOMEDCT);MedicalSubjectHeadings(MeSH);UnifiedMedicalLanguageSystem(UMLS);
Pleasealwaysbeawareofthedefinitionofbiomedicalinformatics(MedizinischeInformatik):BiomedicalInformatics istheinter‐disciplinaryfieldthatstudiesandpursuestheeffectiveuseofbiomedicaldata,information,andknowledgeforscientificinquiry,problemsolving,anddecisionmaking,motivatedbyeffortstoimprovehumanhealth(and well‐being).
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Bayes’RuleBiomedicaldatawarehouseBusinesshospitalinformationsystemClinicalworkflowDataintegrationEnterprisedatamodelingInformationretrieval(IR)ProbabilisticModelQualityofinformationretrievalSettheoreticmodelVectorSpaceModel(VSM)
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Attheendofthisfourthlectureyou…
…haveanoverviewaboutthegeneralarchitectureofanHospitalInformationSystem(detailsinlecture10:MedicalInformationSystemsandBiomedicalKnowledgeManagement);…knowsomeprinciplesofhospitaldatabases;…haveanoverviewonsomebiomedicaldatabases;…arefamiliarwithsomebasicsofinformationretrieval.
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Amongstother problemsomekeychallengesinclude:Increasinglylargeandcomplexdatasets“BigData”duetodataintensiveresearchIncreasingamountsofnon‐standardizedandun‐structuredinformation(e.g.freetext)Dataquality,dataintegration,universalaccessPrivacy,security,safetyanddataprotectionissues(see→Lecture11)Timeaspectsindatabases(Gschwandtner,Gärtner,Aigner &Miksch,2012),(Johnston&Weis,2010).“BigDataresourcesareallawasteoftimeandmoneyifdataanalystscannotfind,orfailtocomprehend,thebasicinformationthatdescribesthedataheldintheresources(Berman,2013b)”DataidentificationiscertainlythemostunderappreciatedandleastunderstoodBigDataissue.Measurements,annotations,properties,andclassesofinformationhavenoinformationalmeaningunlesstheyareattachedtoanidentifierthatdistinguishesonedataobjectfromallotherdataobjectsandthatlinkstogetheralloftheinformationassociatedwiththeidentifieddataobject(Berman,2013a).Communicationofdatabetweenapplicationsystemsmustensuresecuritytoavoidimproperaccess,becausetrustorthelackthereof,isthemostessentialfactorblockingtheadoptionofrapidlyevolvingWebtechnologyparadigmsuchassoftwareasservice(SaaS)ordatadistributionservicessuchasCloudcomputing(Sreenivasaiah,2010).
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Beforewediscussinformationsystems andlearnaboutdatabases,letusstartwithalookintotheHospital…
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Letusstartwithalookintothehospital:Inthisslideweseeatypicalhospitalscenario:medicalprofessionalsaresurroundedbyinformationtechnology.Anolddreamofhospitalmanagerswasalwaystohavean“alldigitalhospital”todigitalizeallworkflowsandtostorealldatainanelectronicway– towardsapaperlesshospital.Althoughmuchefforthasbeenspenttowardsapaperlesshospital,mosthospitalsworldwidearestillfarawayfrombeinga“all‐digitalhospital”(Waterson,Glenn&Eason,2012).Aninterestingstudy:AllhospitalsintheprovinceofStyria(Austria)arewellequippedwithsophisticatedInformationTechnology,whichprovidesall‐encompassingon‐screenpatientinformation.Previousresearchmadeonthetheoreticalproperties,advantagesanddisadvantages,ofreadingfrompapervs.readingfromascreenhasresultedintheassumptionthatreadingfromascreenisslower,lessaccurateandmoretiring.However,recentflatscreentechnology,especiallyonthebasisofLCD,isofsuchhighqualitythatobviouslythisassumptionshouldnowbechallenged.Astheelectronicstorageandpresentationofinformationhasmanyadvantagesinadditiontoafastertransferandprocessingoftheinformation,theusageofelectronicscreensinclinicsshouldoutperformthetraditionalhardcopyinbothexecutionandpreferenceratings.InastudyintheCountyhospitalStyria,Austria,with111medicalprofessionals,workinginareal‐lifesetting,theywereeachaskedtoreadoriginalandauthenticdiagnosisreports,agynecologicalreportandaninternalmedicaldocument,onbothscreenandpaperinarandomlyassignedorder.ReadingcomprehensionwasmeasuredbytheChunkedReadingTest,andspeedandaccuracyofreadingperformancewasquantified.Inordertogetafullunderstandingoftheclinicians'preferences,subjectiveratingswerealsocollected.WilcoxonSignedRankTestsshowednosignificantdifferencesonreadingperformancebetweenpapervs.screen.However,medicalprofessionalsshowedasignificant(90%)preferenceforreadingfrompaper.Despitethehighqualityandthebenefitsofelectronicmedia,paperstillhassomequalitieswhichcannotprovidedelectronicallydodate(Holzingeretal.,2011).BTW:GrazUniversityHospital istheflagshiphospitaloftheStyrianKAGESwith23countyhospitalsandisamongstthelargesthospitalsinEurope.
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Megaissuesrelatedwithhospitalinformationsystemsinclude:dataintegration,datafusion,standardizationissues,clinicalprocessanalysis,modeling,complianceissues,evidencebasedtreatmentanddecisionsupport,privacy,security,safetyanddataprotectionandknowledgediscoveryanddatamining– allconnectedwiththecentraltopicofthislecture:databases.
BTW:TheKAGESusesopenMEDOCS basedonish.med whichisbasedonSAPR3,anoverviewaboutdifferentbusinesshospitalinformationsystemsvendorscanbefoundhere:
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Theteamworkinthehospitalrequiresalotofcommunicationandinformationexchange.Thevisionofabusinessenterprisehospitalinformationsystemistocoverallworkflows,organizationalprocessesandinformationflowselectronically.
Note:Thequalityoftheworkofphysiciansisheavilyinfluencedbytheusabilityoftheiravailableequipment.Intheslideyouseeatypicalworkmeetingofmedicalprofessionals,wheretheydiscussthepatientcasesjointly.Itisimportanttostudyandunderstandtheworkflowsoftheendusersandtoinvolvethemintothedevelopmentofinformationsystemsasearlyaspossiblebyauser‐centereddesignprocess(Holzinger,2003).Experimentsshowedthatbystudyingtheworkflowstheengineersgetdeepinsightsintohowtodevelopanappropriateapplicationforaspecifiedtargetendusergroup(Holzinger,Geierhofer,Ackerl &Searle,2005).
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Theaforementionedgoalofan“all‐digitalhospital”produces“bigdata”andremarkablymuchofthedataisunstructuredtext.Interestingly,themainandmostimportantoutputisthemedicalreport(Arztbrief):Intheexampleitisthereportofamedicalimage– nottheimageitselfistherelevantissue– itisthereport(Holzinger,Geierhofer &Errath,2007b).Thehandlingwithunstructureddataisamegachallengeandbringsalongalotofchallengesforcomputers.
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Letusbrieflycomparehumanintelligencewithmachineintelligence.Agoodexampleonthecomplexitywhichwearefacinginhospitalinformationprocessingarethedifferencesbetweenchessandhumannaturallanguageprocessing:Whereaschessisafinite,mathematicallywell‐definedsearchspace,hencewehaveawelldefinedcomputationalspace,withlimitednumbersofmovesandstatesandgroundedinexplicit,unambiguousmathematicalrules,humanlanguageisexactlytheopposite:Ambiguous,contextualandimplicit;groundedinthehumancognitivespace,withaseeminglyinfinitenumberofwaystoexpressoneandthesamemeaning.Note:IBMDeepBluedefeatedtheWorldChessChampionGarryKasparovinasix‐gamematchin1997.Therewereanumberoffactorsthatcontributedtothissuccess,including:asingle‐chipchesssearchengine,amassivelyparallelsystemwithmultiplelevelsofparallelism,astrongemphasisonsearchextensions,acomplexevaluationfunction,andeffectiveuseofaGrandmastergamedatabase.Technically,DeepBluewasamassivelyparallelsystemdesignedforcarryingoutchessgametreesearches.Thesystemwascomposedofa30‐nodeIBMRS/6000SPcomputerand480single‐chipchesssearchengines,with16chesschipsperSPprocessor.TheSPsystemconsistsof28nodeswith120MHzP2SCprocessors,and2nodeswith135MHzP2SCprocessors.Thenodescommunicatedwitheachotherviaahighspeedswitchandallnodeshad1GBofRAM,and4GBofdisk.Duringthe1997matchwithKasparov,thesystemrantheAIX4.2operatingsystem.ThechesschipsinDeepBluewereeachcapableofsearchingupto2.5millionchesspositionspersecond,andcommunicatewiththeirhostnodeviaafastmicrochannelbus(Campbell,Hoane &Hsu,2002).
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Youcanrememberwhat welearnedlastlectureaboutworkflowsandworkflowmodelling..
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Healthcareprocessesrequirethecooperationofdifferentorganizationalunitsandvariousmedicaldisciplines.Insuchanenvironmentoptimalprocesssupportbecomescrucial.Inthisslideweseeatypicalorganizationalprocessformedicalorderentryandresultreporting,whichisusedtocoordinatetheinter‐departmentalcommunicationbetweenaward(ambulatorysetting)andtheradiologyunit.Thedepictedprocessisnottailoredtoaspecificclinicalpathway,butshowsanexampleforacharacteristicorganizationalprocedureofthehospital:Anorder(inGerman:Anweisung,Verschreibung)isplacedbyaphysicianatthewardoratanambulatorysetting.Theindicationischeckedintheradiologydepartmentanddependingontheresulttheorderplacerisinformedwhethertherequesthasbeenrejectedorscheduled.Theactualradiologicalexaminationandcorrespondingdocumentationisdoneintheexaminationroom.Theradiologyreportisgeneratedafterwards,whichhastobevalidatedbythephysicianwithhissignature.Thereportissentbacktotheorderplacer.Thisisanexampleforafundamentalprocessofclinicalpracticeandcapturestheorganizationalknowledgenecessarytocoordinatethehealthcareprocessamongdifferentpeopleandorganizationalunits;i.e.,focusisonthesupportofcoreorganizationalprocesses(Lenz&Reichert,2007).
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Thisisjustthatyouhave anidea,howcomplicatedsuchprocessescanbeandyoucanimaginehowdifficultitistodigitalizeallinvolveddata
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Themedicaltreatmentprocessisoftendescribedasdiagnostic–therapeuticcycle(Bemmel&Musen,1997)including:observation,reasoning,andaction.Pleaserememberthatinmedicinewedealwithuncertaininformation(Holzinger&Simonic,2011)andeachpassofthediagnostic‐therapeuticcyclecanbeseenasastepindecreasingtheuncertaintyaboutthepatient’sdisease.Consequently,theobservationprocessalwaysstartswiththepatienthistory(“lookingintothepast”)andproceedswithdiagnosticprocedureswhichareselectedbasedonavailableinformation.TheaimoftheHISistoassisthealthcarepersonnelinmakinginformeddecisions.Maybethemostimportantquestiontobeansweredishowtodeterminewhatisrelevant.Availabilityofrelevantinformationisapreconditionfor(good)medicaldecisions– andthemedicalknowledgeguidesthesedecisions(Lenz&Reichert,2007).FollowingtheprinciplesofEvidencebasedmedicine(EBM)physiciansarerequiredtoformulatequestionsbasedonpatients’problems,searchtheliteratureforanswers,evaluatetheevidenceforitsvalidityandusefulness,andfinallyapplythe(new)informationtopatientstreatment(Hawkins,2005).Thelimitingfactoristheshorttimeaclinicianhastomakeadecision(Gigerenzer &Gaissmaier,2011).
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Thisslideshowsaclassicalconceptualmodel:Theheartisacentraldataandcommunicationstructure.Thepatients“enter”(logically)thesystemthroughtheadmissionontheleftside,transferanddischargefunctionsofthecoreandleavesthesystem,atleastpartially,throughtherightside.Inthemainfocusisacentraldatabase,althoughalternativesolutionshaveoptedforamoredistributedconstructionofdatabases;nonethelesscentralorderingprincipleshavetobekepttoachievethenecessaryintegrationofinformationandthedistributiontothevariouspointswhereitisneeded,beitintheareaofhospitalmanagementorinthefieldofcareprovision.Thiscentraldatabaseisservingthecentraloperationalpurposesofthehospitalinthecontextofitsdualgoals(Haux etal.,1998),(Reichertz,2006),(Haux,2006).
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Hereyou seethetypicalarchitectureofsuchasystem
ICU=IntensiveCareUnitNICU=NeonatalIntensiveCareUnitPICU=PediatricIntensiveCareUnit
Therearemanydifferentapplicationarchitecturesinuse,andwewillcometoitbacklater,in→Lecture10,soherejustONEexampleforaenterprisebusinesshospitalinformationsystemasitiscalledprofessionally.However,wewantnowtoconcentrateonsometechnicalissuesofdatabases.
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In ahospitaltherearedata,data,data,…
Inthisclassicalimageby(Shortliffe,Perrault,Wiederhold &Fagan,2001)itbecomesveryobviousthatdatabasesarecentralcomponentsforanhospitalinformationsystem.Averyinterestingslideisthenext,whereweseeanhistoricalexamplefromthe“stone‐age”ofcomputerscience.
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Thispictureby(Gardner,Pryor&Warner,1999)isinsofarinterestingasitshowsusclearlyamegaissueuptothepresent:tointegrateandfusiondifferentdataandtomakeitaccessibletotheclinician.Whilethereismuchresearchontheintegrationofheterogeneousinformationsystems,ashortcomingisintheintegrationofavailabledata.Justtoclarifythedifferencesbetweendataintegrationanddatafusion:
Dataintegrationinvolvescombiningdataresidingindifferentdistributedsourcesandprovidinguserswithaunifiedviewofandaccesstothesedata.Ithasbecomethefocusofextensivetheoreticalandpracticalwork,andnumerousopenproblemsremainunsolved(Lenzerini,2002).Datafusionistheprocessofmergingmultiplerecordsrepresentingthesamereal‐worldobjectintoasingle,consistent,accurate,andusefulrepresentation(Bleiholder &Naumann,2008).ThetrendtowardsP4medicine(Predictive,Preventive,Participatory,Personalized)hasresultedinasheermassofthegenerated(‐omics)data,henceamainchallengeisintheintegrationandfusionofheterogeneousdatasources,especiallyintheintegrationofdatafromtheclinicaldomainwithsourcesfromthebiologicaldomain.
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Integration – datafusion– fordataanalysis– thecentralgoaltosupportdecisionmakingprocesses– datavirtualization– abstractlayer– businessintelligence–serviceorientedarchitecture
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Database(DB)istheorganizedcollectionofdatathroughacertaindatastructure(e.g.hash‐table,adjacencymatrix,graphstructure,etc.).Databasemanagementsystem(DBMS)issoftwarewhichoperatestheDB.WellknownDBMSsinclude:Oracle,IBMDB2,MicrosoftSQLServer,MicrosoftAccess,MySQL,SQLite.ExamplesforGraphDatabasesincludeInfoGrid,Neo4j,orBrightstarDB.TheusedDBisnotgenerallyportable,butdifferentDBMSscaninter‐operatebyusingstandardssuchasSQLandODBC.Databasesystem(DBS)=DB+DBMS.Thetermdatabasesystememphasizesthatdataismanagedintermsofaccuracy,availability,resilience,andusability.Datawarehouse(DWH)isanintegratedrepositoryusedforreportingandlongtermstorageofanalysisdata.DataMarts(DM)areaccesslayersofaDWHandareusedastemporaryrepositoriesfordataanalysis.
RecommendableReadinginclude:(Plattner,2013),(Robinson,Webber&Eifrem,2013):Robinson,I.,Webber,J.&Eifrem,E.2013.GraphDatabases,O'ReillyMedia.Plattner,H.2013.ACourseinIn‐MemoryDataManagement:TheInnerMechanicsofIn‐MemoryDatabases,HeidelbergNewYorkDordrechtLondon,Springer.Oneofthestandardtextbooksisthe6thediton of"DatabaseSystemConcepts"by(Silberschatz,Korth &Sudarshan,2010).
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ADWHisanintegratedsystem,specificallydesignedforenterprisebusinessdecisionsupportandcanbeusedinhospitalsandinbiomedicalapplications.InSlide4‐13weseeanexampleofahospitaldatawarehouse:Onthelefttherearethe(heterogeneous)datasources,suchasPACS(PictureArchiving&CommunicationSystem)andRIS(RadiologicalInformationSystem),andapartfromthecoreHIS,somespecialdatabaseswhichcanalsoincludeproprietaryandlegacysystems.ForthedatastagingandareaserverstheCommonObjectRequestBrokerArchitecture(CORBA)isused,astandarddefinedbytheObjectManagementGroup(OMG)thatsupportsmultipleplatforminteroperability(Zhang,Zhang,Tjandra &Wong,2004).Thisisastandardhospitalinformationarchitectureand– typically‐ withnointegrationoflaboratorydatasourcesandmostofallnoOmics‐dataintegration,asforexamplefromthepathologyorabio‐bank.
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ADWHcanbesubdividedintoso‐calleddatamarts(DM),whichcanbeseenasspecificaccesslayerofaDWH,orientedtoaspecificteam.Slide4‐14showsthearchitectureoftheMayoclinicDWH,whichisincrementallyinstantiatingeachcomponentofthearchitectureondemand.Dataintegrationproceedsfromlefttoright(leftmostyouseetheprimarydatasources;movingright,thedataareintegratedintostagingandreplicationservices,withfurtherrefinement).Thelayersare:1)Subjects=thehighestlevelareasthatdefinetheactivitiesoftheenterprise(e.g.Individual);2)Concepts=thecollectionsofdatathatarecontainedinoneormoresubjectareas(e.g.,Patient,Provider,Referrer,etc.);3)BusinessInformationModels=theorganizationofthedatathatsupporttheprocessesandworkflowsoftheenterprise’sdefinedConcepts.(Chute,Beck,Fisk&Mohr,2010)
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CloudcomputingisagoodexampleforSoftwareasaservice – flexiblespacevianetwork– thisremindsustotheearlydaysofcomputingwithmainframecomputingandthin‐clientterminals.
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AstandardenvironmentforproductionandprocessingofgenomicdatacanbeseeninSlide4‐15:Sequencinglabssubmittheirdatatolargedatabases,e.g.GenBank,NationalCenterofBiotechnologyInformation(NCBI);EuropeanBioinformaticsInstitute(EMBL)database;DNADataBankofJapan(DDBJ);ShortReadArchive(SRA);GeneExpressionOmnibus(GEO)orMicroarraydatabaseArrayExpress.Thesemaintain,organizeanddistributethesequencingdata.Mostusersaccesstheinformationeitherthroughweb‐basedapplicationsorthroughintegrators,suchasEnsembl,theUniversityofCaliforniaatSantaCruz(UCSC)GenomeBrowserorGalaxy.Theendusershavetodownloadgenomicdatafromtheseprimaryandsecondarysources(Stein,2010).Remember:SequencingistheprocessofdeterminingthepreciseorderofnucleotideswithinaDNAmoleculetodeterminetheorderofthefourbases—adenine,guanine,cytosine,andthymine—inastrandofDNA.TheadventofrapidDNAsequencingmethodshasgreatlyacceleratedbiologicalandmedicalresearchanddiscoveryandproduceslargedatasets.Sequencinghasbecomeindispensableforbasicbiologicalresearch,andinnumerousappliedfieldssuchasdiagnosticsandbiotechnology.Note:Abiobank isaphysicalplacewhichstoresbiologicalspecimens– andinsomecasesalsodata(Roden etal.,2008).
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Hereweseeacloud‐basedgenomeinformaticssystem.Insteadofseparategenomedatasetsstoredatvariouslocations,thedatasetsarestoredinthecloudasvirtualdatabases.Webservicesrunontopofthesedatasets,includingtheprimaryarchivesandtheintegrators,runningasvirtualmachineswithinthecloud.Casualusers,whoareaccustomedtoaccessingthedataviatheNCBI,DDBJ,Ensembl orUCSC,workasusual;thefactthattheseserversarelocatedinsidethecloudisinvisibletothem.Poweruserscancontinuetodownloadthedata,buthaveanattractivealternative.Insteadofmovingthedatatothecomputationalcluster,theymovethecomputationalclustertothedata(Stein,2010).Note:Cloudcomputingisbasedonsharingofresourcestoachievecoherenceandeconomiesofscaleoveranetwork(similartotheelectricitygrid).FourAtthefoundationofcloudcomputingisthebroaderconceptofconvergedinfrastructureandsharedservices.Cloudprovidersoffertheirservicesaccordingtoseveralfundamentalmodels1)Infrastructureasaservice(IaaS),2)Platformasaservice(PaaS),3)Softwareasaservice(SaaS)
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Justanexampleforacloudbasedservice:TheMasterIndexisthePACSCloudcoreentityandcontainsinformationaboutothermodules,includingGatewaysandCloudSlaves(repositoryanddatabase).Italsoprovidesauthenticationservicestoinstitutionalgatewaysandallidentifiableinformationrelatedwithpatientsarestoredinamasterindexdatabase,fundamentaltoensuresolutionsforconfidentialityandprivacy.TheCloudSlavesprovide,ononehand,storageofsightlessdata(objectsrepositories)and,onotherhand,adatabasecontainingallnoidentifiablemetadataextractedfromDICOMstudies,i.e.themostdemandingtaskconcerningcomputationalpower(Bastiao‐Silva,Costa,Silva&Oliveira,2011).
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Wehavetodeterminebetweenfederateddataandwarehouseddata.Afederateddatabasesystemisameta‐databasemanagementsystem,whichtransparentlymapsmultipleheterogeneousandautonomousdatabasesystemsintoasinglefederateddatabaseandthiscanbea“virtualdatabase”– withoutdataintegrationasitisindatawarehouses.Intheslidewecanseeonthey‐axisthedataintegrationarchitectureandonthex‐axistheknowledgerepresentationmethodologiesandwherecurrentdataintegrationsystemsliealongthiscontinuum.Theessenceofthisimageisthatthereisno“best‐solution”:Asystemdesignedtohavefullcontrolofdataandfastqueriescanhavedifficultyexpressingcomplexbiologicalconceptsandintegratingthem.SystemsthatemployhighlyexpressiveknowledgerepresentationmethodologiessuchasOntologiesaremoreabletorepresentandintegratecomplexbiologicalconceptsbuthavemuchlesstractablequeries(Louieetal.,2007).
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Obviously thereisadifferencebetweenthedatabasesfortheHospitalInformationSystemandthedatabaseswhichareusedforscientificwork.
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WhereasdatabasesfortheuseinHISareprocesscenteredandcentralfortheelectronicpatientrecord,biomedicaldatabasesarelibrariesofallsortsoflifesciencedata,collectedfromscientificexperimentsandcomputationalanalyses.Suchdatabasescontainexperimentalbiologicaldatafromclinicalwork,genomics,proteomics,metabolomics,microarraygeneexpression,phylogenetics,pharmacogenomics,etc.Examples:Text:e.g.PubMed,OMIM(OnlineMendelian InheritanceinMan);Sequencedata:e.g.Entrez,GenBank (DNA),UniProt (protein).Proteinstructures:e.g.PDB,StructuralClassificationofProteins(SCOP),CATH(ProteinStructureClassification);Anoverviewcanbefoundhere:(Masic &Milinovic,2012),Onlineopenaccessvia:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3544328Note:Pharmacogenomicsisthetechnologyfortheanalyticsofhowgeneticmakeupaffectsanindividual'sresponsetodrugs– soitdealswiththeinfluenceofgeneticvariationondrugresponseinpatientsbycorrelatinggeneexpressionorsingle‐nucleotidepolymorphismswithefficacyandtoxicity.Thecentralaimistooptimizedrugtherapytoensuremaximumeffectivenesswithminimaladverseeffectsandisacoretowardspersonalizedmedicine.
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Agood videocanbeseenhere:https://www.youtube.com/watch?v=DSHhep_w6pk
TheProteinDataBankarchive‐informationaboutthe3Dshapesofproteins,nucleicacids,andcomplexassemblieshelpsstudentsandresearchersunderstandallaspectsofbiomedicine,fromproteinsynthesistohealthanddisease.AsamemberofthewwPDB,theRCSBPDBcuratesandannotatesPDBdata.
TheRCSBPDBbuildsuponthedatabycreatingtoolsandresourcesforresearchandeducationinmolecularbiology,structuralbiology,computationalbiology,andbeyond.
Remember:Proteinsarethemoleculesusedbythecellforperformingandcontrollingcellularprocesses,including:degradationandbiosynthesisofmolecules,physiologicalsignaling,energystorageandconversion,formationofcellularstructuresetc.Proteinstructuresaredeterminedwithcrystallographicx‐raymethodsorbynuclearmagneticresonancespectroscopy.Oncetheatomiccoordinatesoftheproteinstructurehavebeendetermined,atableofthesecoordinatesisdepositedintotheproteindatabase(PDB),aninternationalrepositoryfor3Dstructurefiles:http://www.rcsb.org/pdb/ThisdatabaseishandledbytheRCSB(ResearchCollaboratory forStructuralBiology)attheRutgersUniversityandUCSanDiego.PDBisthemostimportantsourceforproteinstructures.Beforeanewstructureofaproteinisadded,acarefulexaminationofthedatamustbecarriedouttoguaranteethequalityofthestructure.ThePDBdatafilecontains,amongothers,thecoordinatesofalltheatomsoftheprotein(Wiltgen &Holzinger,2005),(Wiltgen,Holzinger&Tilz,2007).
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APDBstructureentryshouldbecitedwithitsPDBIDandprimaryreference.Forexample:PDBID:102LD.W.Heinz,W.A.Baase,F.W.Dahlquist,B.W.Matthews(1993)HowAmino‐AcidInsertionsareAllowedinanAlpha‐Helix
ofT4LysozymeNature361:561.
AnentrywithoutapublishedreferencecanbecitedwiththePDBID,authornames,andtitle:PDBID:1CI0W.Shi,D.A.Ostrov,S.E.Gerchman,V.Graziano,H.Kycia,B.Studier,S.C.Almo,S.K.Burley,NewYorkStructuralGenomiX
ResearchConsortium(NYSGXRC).TheStructureofPNPOxidasefromS.cerevisiae
AnentrymayalsobereferencedusingitsDigitalObjectIdentifier(DOI).TheDOIsforPDBentriesallhavethesameformat:10.2210/pdbXXXX/pdb,whereXXXXshouldbereplacedwiththedesiredPDBID.TheDOIcanbeusedaspartofaURLtoobtainthisdatafile(http://dx.doi.org/10.2210/pdb4hhb/pdb),orcanbeenteredinaDOIresolver(suchashttp://www.crossref.org/)toautomaticallylinktopdb4hhb.ent.gzonthemainPDBftparchive(ftp://ftp.wwpdb.org).Forexample,theDOIforPDBentry4HHBis"10.2210/pdb4hhb/pdb".ThislinksdirectlytotheentryinthePDBfileformatontheFTPserver.ImagesfromStructureSummarypagesshouldcitetheRCSBPDBandthePDBentry:ImagefromtheRCSBPDB(www.rcsb.org)ofPDBID1BNA(H.R.Drew,R.M.Wing,T.Takano,C.Broka,S.Tanaka,K.
Itakura,R.E.Dickerson (1981)StructureofaB‐DNAdodecamer:conformationanddynamicsProc.Natl.Acad.Sci.USA 78:2179‐2183).
ImagescreatedusingPDBdataandothersoftwareshouldcitethePDBIDandthemoleculargraphicsprogramused.Imageof1AOI(K.Luger,A.W.Mader,R.K.Richmond,D.F.Sargent,T.J.Richmond(1997)structureofthecoreparticleat2.8
AresolutionNature389:251‐260)createdwithProteinWorkshop(J.L.Moreland,A.Gramada,O.V.Buzko,Q.Zhang,P.E.Bourne(2005)TheMolecularBiologyToolkit(MBT):amodularplatformfordevelopingmolecularvisualizationapplications.BMCBioinformatics6:21).
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RememberthestructuraldimensionswhichwediscussedinLecture1andLecture2.ThisSlideby(Kampen,2013)isaveryniceoverviewofvariousdatabasesaddressingthedifferentmicroscopicdimensions.Additionally,thedataonthelevelofthehospitalinformationsystemsareadded– sothatyouhaveagoodsummaryoftheaforementioned.IfwetakeasideLiteraturedatabasesandontologies(intheupperrightcornerofthisSlide)westartwith:Genomedatabases:Ensembl http://www.ensembl.org/index.htmlNucleotidesequenceEMBL‐Bankhttp://www.ebi.ac.uk/ena/Geneexpression:ArrayExpress http://www.ebi.ac.uk/arrayexpressProteomes:UniProt http://www.uniprot.org/Proteins:InterPro http://www.ebi.ac.uk/interpro/Proteinstructure:PDBhttp://www.rcsb.org/pdb/home/home.doProteinInteractions:IntAct http://www.ebi.ac.uk/intact/Chemicalentities:ChEMBL https://www.ebi.ac.uk/chembl/Pathways:Reactome http://www.reactome.org/Systems:BioModels http://www.ebi.ac.uk/biomodels‐main/
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Ensembl (nottomixupwithEnsemble;‐)isagoodexampleforaGenomedatabaseandisajointprojectbetweentheEuropeanBioinformaticsInstituteandtheWellcome TrustSangerInstitute,whichwaslaunchedin1999inresponsetotheimminentcompletionoftheHumanGenomeProject(Flicek etal.,2011).Itsaimremainstoprovideacentralizedresourceforgeneticists,molecularbiologistsstudyingthegenomesofourownspeciesandothervertebratesandmodelorganisms.Ensembl providesoneofseveralwell‐knowngenomebrowsersfortheretrievalofgenomicinformation.
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ArrayExpress isadatabaseoffunctionalgenomicsexperimentsthatcanbequeriedandthedatadownloaded.Itincludesgeneexpressiondatafrommicroarrayandhighthroughputsequencingstudies.DataiscollectedtoMIAMEandMINSEQEstandards.ExperimentsaresubmitteddirectlytoArrayExpress orareimportedfromtheNCBIGEOdatabase.MIAME=MinimumInformationAboutaMicroarrayExperiment.Thisisthedatathatisneededtoenabletheinterpretationoftheresultsoftheexperimentunambiguouslyandpotentiallytoreproducetheexperiment(Brazma etal.,2001).ThesixmostcriticalelementscontributingtowardsMIAMEare:1)Therawdataforeachhybridisation (e.g.,CELorGPRfiles),2)Thefinalprocessed(normalised)dataforthesetofhybridisations intheexperiment;3)Theessentialsampleannotationincludingexperimentalfactorsandtheirvalues,4)theexperimentaldesignincludingsampledatarelationships;5)Annotationofthearray(e.g.,geneidentifiers,genomiccoordinates,probeoligonucleotidesequencesorreferencecommercialarraycatalognumber),and6)Laboratoryanddataprocessingprotocols(e.g.,whatnormalisation methodhasbeenusedtoobtainthefinalprocesseddata);see:http://www.mged.org/Workgroups/MIAME/miame.html
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IntAct isanopensourcedatabaseforprotein‐proteininteractions.Thewebinterfaceprovidesbothtextualandgraphicalrepresentationsofsuchproteininteractions,andallowsexploringinteractionnetworksinthecontextoftheGOannotationsoftheinteractingproteins.Moreover,awebserviceallowsdirectcomputationalaccesstoretrieveinteractionnetworksinXMLformat.IntActcontainsbinaryandcomplexinteractionsimportedfromtheliteratureandcuratedincollaborationwiththeSwiss‐Prot team,makingintensiveuseofcontrolledvocabulariestoensuredataconsistency(Hermjakob etal.,2004).http://www.ebi.ac.uk/intact
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TheBioModels Databaseisafreely‐accessibleonlineresourceforstoring,viewing,retrieving,andanalyzingpublished,peer‐reviewedquantitativemodelsofbiochemicalandcellularsystems.Thestructureandbehaviorofeachsimulationmodelarethoroughlychecked;inaddition,modelelementsareannotatedwithtermsfromcontrolledvocabulariesaswellaslinkedtorelevantdataresources.Modelscanbeexaminedonlineordownloadedinvariousformatsandreactionnetworkdiagramscanbegeneratedfromthemodelsinseveralformats.BioModelsDatabasealsoprovidesfeaturessuchasonlinesimulationandtheextractionofcomponentsfromlargescalemodelsintosmallersub‐models.Thesystemprovidesarangeofwebservicesthatexternalsoftwaresystemscanusetoaccessup‐to‐datedatafromthedatabase(Lietal.,2010).http://www.ebi.ac.uk/biomodels/Note:Quantitativemodelsofbiochemicalandcellularsystemsareusedtoanswerresearchquestionsinthebiologicalsciencesanddigitalmodelingisofgrowinginterestinmolecularandsystemsbiology.Awell‐knownexampleistheVirtualHuman(Kell,2007).
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Thelargestmonasterylibraryoftheworld– agoodexampleforawell‐definedknowledgespace.
Yes,perfectlycorrect– thisGoldenRetrieverisbringingbackthewoodenstick– heisretrievingit.Thisisexactlywhatthewordtoretrievemeans:bringingsomethingback.
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Pleaseremember thebasicdifferencesbetweenretrievalanddiscovery:Retrievalisbringingbackanalreadyknownobject,whereasdiscoveryisfindingsomethingwhichwaspreviouslyunknown.Inotherwords:RetrievalisdealingwithknownobjectsandDisovery/Miningisfindingnewthings– inourcasenewinsight(sensemaking)intodata.Slide4‐26makesitclear:
Maimon &Rokach (2010)(Maimon &Rokach,2010)defineKnowledgeDiscoveryinDatabases(KDD)asanautomatic,exploratoryanalysisandmodelingoflargedatarepositoriesandtheorganizedprocessofidentifyingvalid,novel,usefulandunderstandablepatternsfromlargeandcomplexdatasets.DataMining(DM)isthecoreoftheKDDprocess(Witten,Frank&Hall,2011).ThetermKDDactuallygoesbacktothemachinelearningandArtificialIntelligence(AI)community(Piatetsky‐Shapiro,2000).Interestingly,thefirstapplicationinthisareawasagaininmedicalinformatics:TheprogramRxwasthefirstthatanalyzeddatafromabout50,000Stanfordpatientsandlookedforunexpectedside‐effectsofdrugs(Blum&Wiederhold,1985).ThetermreallybecamepopularwiththepaperbyFayyadetal.(1996)(Fayyad,Piatetsky‐Shapiro&Smyth,1996),whodescribedtheKDDprocessconsistingof9subsequentsteps:
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1.Learningfromtheapplicationdomain:includesunderstandingrelevantpreviousknowledge,thegoalsoftheapplicationandacertainamountofdomainexpertise;2.Creatingatargetdataset:includesselectingadatasetorfocusingonasubsetofvariablesordatasamplesonwhichdiscoveryshallbeperformed;3.Datacleansing(andpreprocessing):includesremovingnoiseoroutliers,strategiesforhandlingmissingdata,etc.);4.Datareductionandprojection:includesfindingusefulfeaturestorepresentthedata,dimensionalityreduction,etc.;5.Choosingthefunctionofdatamining:includesdecidingthepurposeandprincipleofthemodelforminingalgorithms(e.g.,summarization,classification,regressionandclustering);6.Choosingthedataminingalgorithm:includesselectingmethod(s)tobeusedforsearchingforpatternsinthedata,suchasdecidingwhichmodelsandparametersmaybeappropriate(e.g.,modelsforcategoricaldataaredifferentfrommodelsonvectorsoverreals)andmatchingaparticulardataminingmethodwiththecriteriaoftheKDDprocess;7.Datamining:searchingforpatternsofinterestinarepresentationalformorasetofsuchrepresentations,includingclassificationrulesortrees,regression,clustering,sequencemodeling,dependencyandlineanalysis;8.Interpretation:includesinterpretingthediscoveredpatternsandpossiblyreturningtoanyoftheprevioussteps,aswellaspossiblevisualizationoftheextractedpatterns,removingredundantorirrelevantpatternsandtranslatingtheusefulonesintotermsunderstandablebyusers;9.Usingdiscoveredknowledge:includesincorporatingthisknowledgeintotheperformanceofthesystem,takingactionsbasedontheknowledgeordocumentingitandreportingittointerestedparties,aswellascheckingfor,andresolving,potentialconflictswithpreviouslybelievedknowledge(Holzinger,2013).
InInformationretrievalaqueryqisdefinedasaformulation(N,L)=qandthematcheswithanindexIMatching(q,I)retrievesrelevantdatatosatisfythesearchquery(Baeza‐Yates&Ribeiro‐Neto,2011).
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Pleaseremember thedifferencesbetweendataobjectsandinformationobjects–dataisanabstractrepresentationinthecomputationalspace– informationisperceivableforthecognitivespace(Notethatitdoesnotmeanthatinformationisautomaticallyknowledge–forgettingknowledgewemustusebothourperceptionandcognition,i.e.humanintelligence)
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AnexcellentstartinthedeterminationbetweenDRandIRistheworkof(VanRijsbergen,1979):ThemostimportantdifferenceisthatthedatamodelinDRisdeterministic,whereaswespeakaboutprobableinformationintheIRModel,henceinformationretrievalisprobabilistic(Simonic&Holzinger,2010).*Monothetic =typeinwhichallmembersareidenticalonallcharacteristics;**Polythetic =typeinwhichallmembersaresimilar,butnotidentical;
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IRcanbedefinedasare‐callofalreadyexistinginformation,notaimingatthediscoveryofnewstructuresasitisthegoalinKnowledgeDiscoveryandDataMining(see→Lecture6).Aswehavealreadyheardseveraltimes,inhospitalinformationsystemsmostofthedataconsistsofmedicaldocuments,whichconsistmostlyofunstructuredinformation:text.But:Whatistext?Fromacomputationalperspective,textconsistsofsequencesofcharacterstrings,thesyntax(Hotho,Nürnberger &Paaß,2005),henceitisanabstractrepresentationofnaturallanguageandthechallengesareinsemantics(meaning).TextprocessingbelongstothefieldofNaturallanguageprocessing(NLP)whichishighlyinterdisciplinary,dealingwiththeinteractionbetweenthecognitivespace(naturallanguages)andthecomputationalspace(formallanguages).Assuch,NLPiscloselyrelatedtoHCI.Textminingisasubfieldofdatamining.TheoriginalgoalofIRwastofinddocumentswhichcontainanswerstoquestionsandnotthefindingofanswersitself(Hearst,1999).Forthispurposestatisticalmeasuresandmethodsareused,andweneedaformaldescriptionfirst.
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Thisisthegeneralprinciple:Theenduserformulateshisqueryviatheuserinterface,informofaTextOperations(“userneed”).Thenextstepistherepresentation(logicaldocumentviewDintheformalmodelin→Slide4‐30)ofthedocumentsandtherepresentationofthereasoningstrategy,querylogicalviewQ(comparewith→Slide4‐30and→Slide4‐31).Theresultisarankingoftheretrieveddocuments,whichwillbedisplayedviatheuserinterface.
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52
ModelingtheIR‐processiscomplex,becausewearedealingwithimprecise,vagueanduncertainelements,thusitisdifficulttoformalizeduetohighinfluencesofhumanfactors,i.e.relevanceandinformationneeds,whicharehighlysubjectiveandcontextspecific.However,inthedefinitionofanyIR‐modelwecanidentifysomecommonaspects(Canfora &Cerulo,2004).Thefirststepistherepresentationofdocumentsandinformationneeds.Fromtheserepresentationsareasoningstrategycanbedefined,whichsolvesarepresentationsimilarityproblemtocomputetherelevanceofdocumentswithrespecttothequeries.VariousstrategieshavebeenintroducedwiththeaimofimprovingtheIR‐process.Weclassifythesemethodologiesundertwomainaspects:Representation(query&document,seeSlide→4‐33)andReasoning(applicationofdiversemethods,see→Slide4‐34).LettheIRModelbeaquadruple
Eq.4‐1 IR={D,Q,F,R(q_i,d_j)}
Disasetcomposedoflogicalviews(representationcomponent)ofthedocumentswithinacollection;Qisasetoflogicalviews(representationcomponent)oftheuserinformationneeds(thesearecalledqueries);Fisaframeworkformodelingdocumentrepresentations,queriesandtheirrelationships(reasoningcomponent);ThisincludessetsandBooleanrelations,vectorsandlinearalgebraoperations,samplespacesandprobabilitydistributions;R(qi,dj)isarankingfunction(→Slide4‐31)thatassociatesarealnumberwithaqueryrepresentationqi Qandadocumentrepresentationdj D.Suchrankingdefinesanorderingamongthedocswithregardtothequeryqi.Theenduserin→Slide4‐29formulateshisqueryinformofatextoperation,thenextstepistherepresentation(logicalviewD)ofthedocumentsandtherepresentationofthereasoningstrategy,bothlogicalviewsDandQ(comparewithSlide4‐31)resultinarankingoftheretrieveddocuments.
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ThelogicalviewsDandQresultintherankingfunctionR(qi,dj)accordingto(Baeza‐Yates&Ribeiro‐Neto,2011)
Speak:Rindexed dsubscriptjandqsubscripti
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Guess whichalgorithmthisis?AshortdescriptioncanbefoundinHastie,T.,Tibshirani,R.&Friedman,J.2009.TheElementsofStatisticalLearning:DataMining,Inference,andPrediction.SecondEdition,NewYork,Springer.
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Yes!Alot differentmethods– everymethodhavingparticularadvantagesanddisadvantages– wecannotdiscussmuchhere,butwecangetaroughoverview.
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TherepresentationcomponentisanessentialpartofeveryIRsystem,asitistherepresentationoftheinformationitself(visibletotheuser):informationcanbeprocessedifitisrepresentedinanappropriateway.Queriesaretherepresentationofinformationneedsofauser.Note:Atextcanbecharacterizedbyusingfourattributes:syntax,structure,semantics,andstyle.Atexthasagivensyntaxandastructure,whichareusuallydictatedbytheapplicationorbythepersonwhocreatedit.Textalsohassemantics,specifiedbytheauthorofthedocument.Additionally,adocumentmayhaveapresentationstyleassociatedwithit,whichspecifieshowitshouldbedisplayedorprinted.Inmanyapproachestotextrepresentationthestyleiscoupledwiththedocumentsyntaxandstructure(LaTeX).XMLseparatestherepresentationofsyntaxandstructures,definedeitherbyaDTDoranXSD,andstyle,whichiscapturedbyXSL(Canfora &Cerulo,2004).Note:Ann‐gramisasubsequenceofnitemsfromagivensequence.Theitemsinquestioncanbephonemes,syllables,letters,wordsorbasepairsaccordingtotheapplication.Ann‐gramofsize1isreferredtoasa"unigram";size2isa"bi‐gram"(or,lesscommonly,a"di‐gram");size3isa"tri‐gram";size4isa"four‐gram"andsize5ormoreissimplycalledan"n‐gram".Somelanguagemodelsbuiltfromn‐gramsare"(n−1)‐orderMarkovmodels".Ann‐grammodelisatypeofprobabilisticmodelforpredictingthenextiteminsuchasequence.n‐grammodelsareusedinvariousareasofstatisticalnaturallanguageprocessingandgeneticsequenceanalysis.
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Deeplearningalgorithmsarebasedondistributedrepresentations,withtheassumptionthatobserveddataisgeneratedbytheinteractionsofmanydifferentfactorsondifferentlevels.Deeplearningaddstheassumptionthatthesefactorsareorganizedintomultiplelevels,correspondingtodifferentlevelsofabstractionorcompositionandvariousnumbersoflayersandlayersizescanbeusedtoprovidedifferentamountsofabstraction.Bengio,Y.;Courville,A.;Vincent,P.(2013)."RepresentationLearning:AReviewandNewPerspectives".IEEETransactionsonPatternAnalysisandMachineIntelligence35(8):1798–1828
Reasoningreferstothesetofmethods,models,andtechnologiesusedtomatchdocumentandqueryrepresentationsintheretrievaltask.Strictlyrelatedwiththereasoningcomponentistheconceptofrelevance.TheprimarygoalofanIRsystemistoretrievethedocumentsrelevanttoaquery.Thereasoningcomponentdefinestheframeworktomeasuretherelevancebetweendocumentsandqueriesusingtheirrepresentations(Canfora &Cerulo,2004).Google,forexample,usesakeywordbasedvectorspacemodel(see→Slide4‐38)alongwithgraph‐basedprobabilitytheoriesandFuzzysettheories.Slide4‐35showsaconciseoverviewofsomeselectedmethods,accordingtovariousdocumentproperties.
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TherearemanymethodsofIR,fordetailsconsultastandardreferencee.g.Baeza‐Yates&Ribeiro‐Neto (2011).SettheoreticapproachesincludetheClassicSet‐basedBoolean,theExtendedBooleanandtheFuzzyApproach;AlgebraicapproachesincludetheGeneralizedVectorModel,LatentSemanticIndexing(LSI),NeuralNetworks;andtheProbabilisticapproachincludesBayesianNetworks,LanguageModelsandInferenceNetworks.Wewilldiscussonlyafewandtheseverybriefly,sothatyouhaveaquickoverview:Thesettheoreticapproach:BooleanModelinSlide4‐36andSlide4‐37;theVectorSpaceModelinSlide4‐38toSlide4‐42;andtheProbabilisticModelinSlide4‐43toSlide4‐44.
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Documents/queriesarerepresentedasasetofindexterms;queriesareBooleanexpressions(AND,OR,NOT);FortheBooleanmodel,theindextermweightvariablesarebinary,i.e.w_(i,j)∈{0│1}.AqueryqisaconventionalBooleanexpression.Letq _dnf bethedisjunctivenormalformofthequeryq.Further,letq_ccbeanyoftheconjunctivecomponentsofq _dnf.Thesimilarityofadocumentd_j tothequeryqisdefinedas
Ifsim(d_j,q)=1thentheBooleanmodelpredictsthatthedocumentd_j isrelevanttoqueryq.Otherwisethepredictionisthatthedocumentisnotrelevant.Fordetailspleasereferto(Baeza‐Yates&Ribeiro‐Neto,2011)
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TheBooleanModelhasseveraladvantages,includingeasytounderstand,exactformalismandthequerylanguageisexpressive;however,seriousdisadvantages,e.g.nopartialmatches,the“bag‐of‐words”representationdoesnotaccuratelyconsiderthesemanticsofdocuments(Vallet,Fernández &Castells,2005),andthequerylanguageiscomplicated,finallytheretrieveddocumentscannotberanked.
TheExtendedBooleanModel(EBM)by(Salton,Fox&Wu,1983)overcomessomedisadvantagesbymakinguseofpartialmatchingandtermweights,similarasinthevectorspacemodel.Moreover,asthevector‐processingsystemsuffersfromonemajordisadvantage:thestructureinherentinthestandardBooleanqueryformulationisabsent,theEBMcombinesthecharacteristicsoftheVectorSpaceModelwiththepropertiesofBooleanalgebra.Hence,theEBMcanalsobeapplied,whentheinitialquerystatementsareavailableasnaturallanguageformulationsofuserneeds,ratherthanasconventionalBooleanformulations.
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Thevectorspacemodel(VSM)representsdocumentsasvectorsinthem‐dimensionalspace(Salton,Wong&Yang,1975).Thus,documentscanbecomparedbyvectoroperationsandqueriescanbeperformedbyencodingthequerytermssimilartothedocumentsinaqueryvector.Thisqueryvectorcanbecomparedtoeachdocument,whichreturnsaresultlistbyorderingthedocumentsaccordingtothecomputedsimilarity.Themaintaskofthevectorspacerepresentationofdocumentsistofindanappropriateencodingofthefeaturevector.Eachelementofavectorusuallyrepresentsaword(see→Slide4‐40)ofthedocumentcollection.Thesizeofthevectorisdefinedbythenumberofwordsofthecompletedocumentcollection.Theeasiestwayofdocumentencodingistousebinarytermvectors,thatmeansavectorelementissetto1ifthecorrespondingwordisusedinthedocumentandto0ifthewordisnot(Equation4‐4).ThisencodingresultsinasimpleBooleancomparison.Toimprovetheperformanceusuallytermweightingschemesareused,wheretheweightsreflecttheimportanceofawordinaspecificdocumentoftheconsideredcollection.Largeweightsareassignedtotermsthatareusedfrequentlyinrelevantdocumentsbutrarelyinthewholedocumentcollection(Salton&Buckley,1988).Thusaweightw(d;t)foratermtindocumentdiscomputedbytermfrequencytf (d;t)timesinversedocumentfrequencyidf(t),whichdescribesthetermspecificitywithinthedocumentcollection.TherankingcanbemadebyusingtheCosinesSimilarity(see→Slide4‐41).Thecosineoftheanglebetweentwovectorsisameasureofhow“similar”theyare,whichinturn,isameasureofthesimilarityofthesestrings.Ifthevectorsareofunitlength,thecosineoftheanglebetweenthemissimplythedotproductofthevectors(Tata&Patel,2007).
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Asaresultwegetamatrixrepresentation,andnowwecanapplyvectoralgebra,orparticularlinearalgebra– herestillinR3.Mathematically,wecanworkinarbitrarilyhighdimensionalspaces.ThemajorprobleminvolvedisthemappingbackintoR2.Oneverypositiveaspectisthatwecanlookforgettingsparsematrices,i.e.wesavealotofcomputationalpower.
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Turney,P.D.&Pantel,P.2010.Fromfrequencytomeaning:Vectorspacemodelsofsemantics.Journalofartificialintelligenceresearch,37,(1),141‐188.
Computersunderstandverylittleofthemeaningofhumanlanguage.Thisprofoundlylimitsourabilitytogiveinstructionstocomputers,theabilityofcomputerstoexplaintheiractionstous,andtheabilityofcomputerstoanalyseandprocesstext.Vectorspacemodels(VSMs)ofsemanticsarebeginningtoaddresstheselimits.Turney etal. (2010)surveystheuseofVSMsforsemanticprocessingoftext.TheyorganizetheliteratureonVSMsaccordingtothestructureofthematrixinaVSM.TherearecurrentlythreebroadclassesofVSMs,basedonterm–document,word–context,andpair–patternmatrices,yieldingthreeclassesofapplications.Theysurveyabroadrangeofapplicationsinthesethreecategoriesandwetakeadetailedlookataspecificopensourceprojectineachcategory.TheirgoalinthissurveyistoshowthebreadthofapplicationsofVSMsforsemantics,toprovideanewperspectiveonVSMsforthosewhoarealreadyfamiliarwiththearea,andtoprovidepointersintotheliteratureforthosewhoarelessfamiliarwiththefield.
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Beim Retrievalverfahren wird ein Rankingähnlicher Dokumente über dieCosinusÄhnlichkeit im m‐Dimensionalen Vektorraum durchgeführt.
InformationNeedQ→ =( _1"," _2,…"," _ )Wird ein Rankingähnlicher Dokumente über dieCosinus Ähnlichkeit im mdimensionalen VectorSpaceModeldurchgeführt
DerVorteil dieser Methode ist,dass es ein einfaches mathematisches Modelldarstellt,DieMatrizen sind Sparse(ist alsoeine günstige Datenstruktur)Dasretrievalkann inO(n)durchgeführt werden,daher gibt es ein relativ schnellesranking
Nachteile:DieWortanordung geht verloren (BagofWordAnsatz).
Es gibt viele weitere Methoden,wie z.B.LatentSemanticAnalysis(LSA)usw.ProbabilisticLatentSemanticAnalysis(PLSA)LatentDirichlet Allocation(LDA)
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TheadvantagesofthealgebraicVSMincludethatitiseasytounderstand,partialmatchesarepossible,documentscanbesortedbyrank,anditusesterm‐weightingschemes;ontheothersidethereisahighercomputationalefforttocalculatesimilarity,andthe“bag‐of‐words”representationdoesnotaccuratelyconsiderthesemanticsofdocuments(Vallet,Fernández &Castells,2005).
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Fortheprobabilisticmodel,theindexweightvariablesareallbinary,i.e.ωij∈[0,1],ωiq∈[0,1].Aqueryqisasubsetofindexterms.LetRbethesetofdocumentsknown(orinitiallyguessed)toberelevant.LetR̅bethecomplementofR(thisisthesetofnon‐relevantdocuments).LetP(R/dj)bedeprobabilitythatthedocumentdj isrelevanttothequeryqandP(R̅/dj)betheprobabilitythatdj isnonrelevanttoq.Thesimilaritysim(dj,q)ofthedocumentdj tothequeryqisdefinedastheratio:
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Asinallmodelswehavecertainprosandcons,theprobabilisticmodelhasabigadvantage:thedocumentscanberankedbyrelevance;however,onthedisadvantageoussideitisabinarymodel(binaryweights),theindextermsareassumedtobeindependentandlackofdocumentnormalizationandthereisaneedtoguesstheinitialseparationofdocumentsintorelevantandnon‐relevantsets.
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Well, therearetwomainmeasurements
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Recall andPrecision– hardasabone
Followingthisdefinition:Recall=Correct/(Correct+Missing)andPrecision=Correct/(Correct+Spurious)
PrecisionPisthefractionofretrieveddocumentsthatarerelevanttothesearch:P=|{setofrelevantdocs}∩{setoffounddocs}|/{setoffounddocs}RecallRisthefractionofthedocumentsthatarerelevanttothequerythataresuccessfullyretrieved:R=|{setofrelevantdocs}∩{setoffounddocs}|/{setofrelevantdocs}Acombinationofprecisionandrecallistheharmonicmeanofboth,whichiscalledF‐measure:F=2∙(P∙R)/(P+R)Inclassification5termsareused:truepositives(=correct);truenegatives(=correct);falsepositives(=spurious);falsenegatives(=spurious);notdetected(=missing).
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Inthisslideweseeanoverviewofthelinguisticprocessingpipelinethatdescribesthestepsthatareperformedfromthedocumenttoitssemanticrepresentation.ThedomainknowledgeusedinthesemanticretrievalsystemismodeledintheformofthemedicalsemanticnetworkIDMACSR(MSN).ItusestheWingert Nomenclature(WNC)asitsmedicalterminology.TheWNCisbasedontheGermanversionofSNOMEDdevelopedbyFriedrichWingert.AlthoughitsmainfocusisonGerman,it,toalesserextent,supportsseveralotherlanguagesincludingEnglishandFrench.TheMSNformsasimpleontologywhoseconceptsareorganizedinataxonomy(isA‐hierarchy)andamerology (anatomicalpartOfhierarchy).Furtherrelationsbetweenconceptsaremodeled bylabelededges.TheMSNisdividedintoseveralsubdomains,including:– topography(i.e.,anatomicalconcepts)– morphology(e.g.,fracture,fever)– function(e.g.,respiration)– diseases(e.g.,glaucoma)– agents(e.g.,pathogens,pharmaceuticalsubstances)Currently,theMSNcontainsmorethan90,000termsand300,000uniquerelations.Thequerylanguagefollowsasimplegrammar,namely:Query::=DisjunctionDisjunction::=Conjunction|Conjunction";"DisjunctionConjunction::=Atom|Atom","ConjunctionAtom::=Term|"!"TermThusaqueryformsaBooleanexpressionindisjunctiveformoversearchterms.Semanticqueryexpansionhasbeendiscussedinseveralpreviouswork(Kingsland,Harbourt,Syed&Schuyler,1993),(Aronson,Rindflesch &Browne,1994)(Efthimiadis,1996).Theapproachisasfollows:eachsearchtermisindexed(usingthelinguisticprocessingmethodsdescribedabove)andreplacedbytheidentifieroftheWNCconceptmatchingtheterm.TheseconceptidentifiersarecalledWNCindices.IfthesearchtermreferstoacombinationofseveralconceptsintheWNC(e.g.,Gastroparesis=Stomach+Paresis),thesearchtermisreplacedbyaconjunctionoftheWNC(Kreuzthaleretal.,2011).
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AscanbeseenfromthisSlidethemedicaldomainexpertoutperformstheotherretrievalmethods,achievinghighprecisionatahighrecalllevel.Interestingly,thesemanticbasedinformationretrievaltoolachievesapproximatelythesamerecalllevelasthemedicaldomainexpertwhilehavingalowerprecisionvalue.Thisperformanceresultisgood,rememberingthefactwhateffortthemedicaldomainexperthastomaketotranslatetheinformationneedintoaquerystring.Incontrasttothis,theinputfortheinformationretrievaltoolisshortandclearsothereforelessefforthastobemadetotransformtheinformationneedtothequerylanguageunderstoodbytheinformationretrievaltool.Keywordsearchhasahighprecisionvaluebutalowerrecallvalue.Thisresultisclearwhenconsideringthefactthatinformationneedsthatcanbedescribedbyusingthesekeyword(s)willachieveahighprecisionvalue.So,ifdocumentsarefoundtheywillberelevantbuttherecalllevelwillgenerallysuffer.LookingattheSlide4‐47,keywordsearchachievesapproximatelythesameprecisionasIRToolOnebutafarworserecall.ItisalsopossiblethatnosearchresultsarefoundatallwhenusingthekeywordsearchmethodologyascanbeseenfortheNeubildung,Darm informationneed(seeAppendixBandAppendixA).Incontrasttothis,forthisinformationneed,IRToolOnehasaboutthesameprecisionrecalllevelsasthemedicaldomainexpert,reflectingthesemanticprocessingchainofthetool.TheLSAstatisticalretrievalmethodhas,whencomparedtotheothermethods,alowerprecisionforallmeasuredrecalllevels.ThisresultgivestheimpressionthatLSAisapplicableforgettinghighprecisionvaluesforaparticularamountofsearchresultsbuthardtousetoachievebothhighprecisionandhighrecallvalues,whichisneededforexampleinclinicalstudies.
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Thefutureofbigdatais…big andtherewillbemanychallengesforus tosolve!
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The grandquestionsofthefutureishowtomakesenseoutofthedata– megaquestionsincludeare:“Whatisinteresting?”– and“Whatisrelevant?”
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Adverse DrugEvents(ADE)areverycommonandthereforetheorderentrymustbetakenspecialcareof.Themedicationordersindifferentmedicationsystems.(a)Kardex system,(b)TIMEDsystem,and(c)CPOEsystem.
Physiciansmustentertheirmedicationordersintothesystem;nursesmaynotacceptanyhand‐writtenprescription.Aphysicianentersamedicationorderbyselectingadruganditsdosageform,strength,administrationroute,dosageregimen,startdateandtime.
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ComparisonshowedthatthemedicationorderingandadministrationprocessaftertheimplementationresemblesthatoftheKardex‐system,whileitiscompletelydifferentfromthatoftheTIMED‐system.InbothKardex andTIMEDunits,wecomparednurseattitudestowardsthecomputerizedprocessinthepost‐implementationphasewiththeirattitudestowardsthepaper‐basedprocessinthepreimplementation phase.
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NO=NoStock
ThemedicationorderingandadministrationprocessesinKardex‐systemandTIMED‐system;MO(MedicationOrder);HIS(HospitalInformationSystem);NS(Non‐Stock);forrequestingurgentNSdrugs,nursesoftendirectlyreferredtothepharmacywithhand‐writtenrequests.
ComparisonofFigs.2and3showsthatthemedicationorderingandadministrationprocessaftertheimplementationresemblesthatoftheKardex‐system,whileitiscompletelydifferentfromthatoftheTIMED‐system.InbothKardex andTIMEDunits,wecomparednurseattitudestowardsthecomputerizedprocessinthepost‐implementationphasewiththeirattitudestowardsthepaper‐basedprocessinthepreimplementation phase.
Thereisnocleardefinitionaboutthis,butitisdefinitelyaboutmanagementofdata,informationandknowledgefordecisionsupport.Letuslookintoapracticalexample– physicianorder– wherealotoferrorshappenedinthepastduetoamessofpaperbasedordersproducingalotofpaperchaos(youallknowthepost‐itsyndrome)
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Observationsandresultsofinvestigations—includinghistory,signs,andsymptoms—areconvertedbyclinicalstaffintodecisionsandappropriateactions.Controlusuallyrequirestheuseofrecordsandexternalsourcesofknowledge
Thecareofeachpatientcanbeconsideredtobeacontrolloopin whichdatafromobservationsandinvestigationsleadtodecisionsandactionsdesignedtotakecareofapatient'sproblemsandtheirconsequencesinasafe,effective,andlegitimatemanner.Thisloopoccursinallspecialtiesandisthesourceofalltheactivitiesofahealthcarefacilitysuchasahospital.Thoughcomplex,theseactivitiescanbesetoutasfourconcentricshells.
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Theclinicalcontrolloopisatthecoreofacomplexorganisation representedbyfour“shells”thatexchangedata.Activity shellsofclinicalcontrolloop
Clinicalmanagementshell—Assessmentofobservationsandresultsofinvestigations.Formulationsofdecisionsincludingthosebasedonobservations,investigations,andprocedurescarriedoutduringaconsultation
Clinicaladministrativeshell—Administrativeactivitieswhichfacilitatetheclinicalmanagementshellandlinkittotheothershells,suchasarrangingappointmentsandinvestigations,clinicalcorrespondence,filingresults,andclinicalaudit
Clinicalservicesshell—Investigative,therapeutic,andgeneralservicesprovidedbylaboratories,imagingfacilities,therapyunits,operatingtheatres,wards,suppliesdepartments,transport,etc
Generalmanagementshell—Generalmanagementofhealthcare,byhospitalmanagers,financialcontrollers,healthcarepurchasers,andstatutoryauthorities
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Exampleofavisualizedinformationsystemarchitecture,hereofthecomputer‐supportedpartofthehospitalinformationsystemoftheMedicalSchoolHanoverfrom1984([1],p.9).
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Mayo’sEnterpriseDataModeling(EDM)providesacontextforMayoenterpriseactivities.
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Care2x1isagenericmulti‐languageopen‐sourceprojectthatimplementsamodernHospitalInformationSystem.TheprojectwasstartedinMay2002withthereleaseofthefirstbetaversionofCare2xbyanursewhowasdissatisfiedwiththeHISinthehospitalwherehewasworking.Untiltodaythedevelopmentteamhasgrowntoover100membersfromover20countries.Care2xisaweb‐basedHISthatisbuiltuponotheropen‐sourceprojects:theApachewebserverfromtheApacheFoundation(http://www.apache.org/),thescriptlanguagePHP(http://www.php.org/)andtherelationaldatabasemanagementsystemmySQL(http://www.mysql.com/).ThereexistseveralsourcecodebranchesthattrytointegratetheoptiontochoosefromotherRDBMSlikeOracleandpostgreSQL.Thelatteroneisalreadysupportedinthecurrentversionatthetimeofwriting:“deployment2.1”.Forourinvestigationswehavechosenthemostfeature‐richversionthatwasavailablefromtheCare2xwebpageinearlyfall2004.Thisreleasehadtheversionnumber“pre‐deployment2.0.2”.Someminordeficienciesthatwereportlatermayalreadybefixedinthecurrentversion“deployment2.1”.
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This isjusttoshowyouanexampleofaglobaldatabaseschemaEachmolecule(“Molecules”table)mayhavemorethanoneconformation(“Conformations”table)anditmaycomefrommorethanonesource(“Sources”table).Therearetwotypesofexperiments(“Experiments”table)thataredoneonmolecules:computationaldockingandbiologicalassays.Theresults(“DockingResults”and“AssayResults”tables)oftheseexperimentswerecapturedinthedatabase.Eachtypeofexperimentisdoneonaparticularp53mutant(“Mutants”table)andhasascore(“Scores”table)associatedwithit.
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OpenDatabase Connectivity– APIinCforaccessingDBMSSystemarchitectureandthehybridstrategytodataintegration.Dockingandsmallmoleculedatausethemediationapproach,whilethefunctionalandstructuralassaydatausethedatawarehousingapproach.TheCRDBisbothamediatorandadatawarehouse.“Mutants”and“Molecular”aredatamartsofthewarehouse.TheODBCdriversarewrappersinthemediationapproach.Dashedlinesindicateintegrationplannedinthefuture.
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Theatomiccoordinatesofaproteinaredepositedintotheproteindatabase(PDB),aninternationalrepositoryfor3Dstructurefiles.AtthemomentPDBcontainsmorethan26.000proteinstructures
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Wewilldealwithvisualizationsinlecture9– herejustanappetizerwhatyoucandisplay
Thisshows acervicalcancerqueryvisualization.TheGenenodesarepositionedusingbothchromosomenumberandorganismname.ThispositioningmethodallowsuserstofocusonaparticulargeneandspeciesusingNVSS’ssliderfilters.Nodesaresize‐codedaccordingtotheirindegree,whichprovidesanadditionalvisualcueaboutthenode’simportance.
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http://psychology.wikia.com/wiki/Information_retrievalhttp://www.eecs.wsu.edu/mgd/gdb.html(GraphDatasets)
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