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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A. Holzinger                                                         LV 709.049                                         Mi, 04.11.2015

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A. Holzinger                                                         LV 709.049                                         Mi, 04.11.2015

Bayes’RuleBiomedicaldatawarehouseBusinesshospitalinformationsystemClinicalworkflowDataintegrationEnterprisedatamodelingInformationretrieval(IR)ProbabilisticModelQualityofinformationretrievalSettheoreticmodelVectorSpaceModel(VSM)

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A. Holzinger                                                         LV 709.049                                         Mi, 04.11.2015

Attheendofthisfourthlectureyou…

…haveanoverviewaboutthegeneralarchitectureofanHospitalInformationSystem(detailsinlecture10:MedicalInformationSystemsandBiomedicalKnowledgeManagement);…knowsomeprinciplesofhospitaldatabases;…haveanoverviewonsomebiomedicaldatabases;…arefamiliarwithsomebasicsofinformationretrieval.

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A. Holzinger                                                         LV 709.049                                         Mi, 04.11.2015

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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A. Holzinger                                                         LV 709.049                                         Mi, 04.11.2015

Beforewediscussinformationsystems andlearnaboutdatabases,letusstartwithalookintotheHospital…

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A. Holzinger                                                         LV 709.049                                         Mi, 04.11.2015

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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14

Theteamworkinthehospitalrequiresalotofcommunicationandinformationexchange.Thevisionofabusinessenterprisehospitalinformationsystemistocoverallworkflows,organizationalprocessesandinformationflowselectronically.

Note:Thequalityoftheworkofphysiciansisheavilyinfluencedbytheusabilityoftheiravailableequipment.Intheslideyouseeatypicalworkmeetingofmedicalprofessionals,wheretheydiscussthepatientcasesjointly.Itisimportanttostudyandunderstandtheworkflowsoftheendusersandtoinvolvethemintothedevelopmentofinformationsystemsasearlyaspossiblebyauser‐centereddesignprocess(Holzinger,2003).Experimentsshowedthatbystudyingtheworkflowstheengineersgetdeepinsightsintohowtodevelopanappropriateapplicationforaspecifiedtargetendusergroup(Holzinger,Geierhofer,Ackerl &Searle,2005).

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A. Holzinger                                                         LV 709.049                                         Mi, 04.11.2015

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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A. Holzinger                                                         LV 709.049                                         Mi, 04.11.2015

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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A. Holzinger                                                         LV 709.049                                         Mi, 04.11.2015

Thefutureofbigdatais…big andtherewillbemanychallengesforus tosolve!

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A. Holzinger                                                         LV 709.049                                         Mi, 04.11.2015

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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A. Holzinger                                                         LV 709.049                                         Mi, 04.11.2015

ComparisonshowedthatthemedicationorderingandadministrationprocessaftertheimplementationresemblesthatoftheKardex‐system,whileitiscompletelydifferentfromthatoftheTIMED‐system.InbothKardex andTIMEDunits,wecomparednurseattitudestowardsthecomputerizedprocessinthepost‐implementationphasewiththeirattitudestowardsthepaper‐basedprocessinthepreimplementation phase.

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A. Holzinger                                                         LV 709.049                                         Mi, 04.11.2015

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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A. Holzinger                                                         LV 709.049                                         Mi, 04.11.2015

Observationsandresultsofinvestigations—includinghistory,signs,andsymptoms—areconvertedbyclinicalstaffintodecisionsandappropriateactions.Controlusuallyrequirestheuseofrecordsandexternalsourcesofknowledge

Thecareofeachpatientcanbeconsideredtobeacontrolloopin whichdatafromobservationsandinvestigationsleadtodecisionsandactionsdesignedtotakecareofapatient'sproblemsandtheirconsequencesinasafe,effective,andlegitimatemanner.Thisloopoccursinallspecialtiesandisthesourceofalltheactivitiesofahealthcarefacilitysuchasahospital.Thoughcomplex,theseactivitiescanbesetoutasfourconcentricshells.

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A. Holzinger                                                         LV 709.049                                         Mi, 04.11.2015

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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A. Holzinger                                                         LV 709.049                                         Mi, 04.11.2015

Exampleofavisualizedinformationsystemarchitecture,hereofthecomputer‐supportedpartofthehospitalinformationsystemoftheMedicalSchoolHanoverfrom1984([1],p.9).

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Mayo’sEnterpriseDataModeling(EDM)providesacontextforMayoenterpriseactivities.

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A. Holzinger                                                         LV 709.049                                         Mi, 04.11.2015

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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A. Holzinger                                                         LV 709.049                                         Mi, 04.11.2015

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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A. Holzinger                                                         LV 709.049                                         Mi, 04.11.2015

OpenDatabase Connectivity– APIinCforaccessingDBMSSystemarchitectureandthehybridstrategytodataintegration.Dockingandsmallmoleculedatausethemediationapproach,whilethefunctionalandstructuralassaydatausethedatawarehousingapproach.TheCRDBisbothamediatorandadatawarehouse.“Mutants”and“Molecular”aredatamartsofthewarehouse.TheODBCdriversarewrappersinthemediationapproach.Dashedlinesindicateintegrationplannedinthefuture.

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A. Holzinger                                                         LV 709.049                                         Mi, 04.11.2015

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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A. Holzinger                                                         LV 709.049                                         Mi, 04.11.2015