an informatics architecture for an exposome v1...
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DepartmentofBiomedicalInformatics
BiomedicalInformaticsCoreCenterforClinicalandTranslationalSciences
AnInformaticsArchitectureforanExposome
SessionII06– SecondaryUseofDataforResearch(InteractiveLearning)AMIA2016JointSummitsonTranslationalScience
March22nd,2016
Dr.RamGouripeddiAssistantProfessor,DepartmentofBiomedicalInformaticsChiefBiomedicalInformaticist,BiomedicalInformaticsCore,
CenterforClinicalandTranslationalScienceUniversityofUtah
Acknowledgements
• PRISMSPI(s)• KatherineSward,RN,PhD• JulioC.Facelli,PhD
• PRISMSTeamatUniversityofUtah• Nodisclosures
This effort was supported by U54EB021973, National Institute of Biomedical Imaging and Bioengineering, NIH andUniversity of Utah Air Quality Seed Grant Program. OpenFurther supported by NCRR/ NCATS Grants UL1RR025764 and3UL1RR025764-02S2, National Center for Clinical and Translational Science 1UL1TR001067, University of Utah ResearchFoundation, grant 1D1BRH20425 (DHHS), and R01 HS019862 from AHRQ, (DHHS).
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Overview
• EffectsofEnvironmentonHealth• KeyConcepts• InitialWork– AMIA2014• Limitations• ChallengesandInformaticsMethodsandSolutions• PRISMS• InformaticsArchitecture
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EffectsofEnvironmentonHealth• Phenotype:Resultofinteractionsbetweengenotypeandenvironment.• Environmentalfactorscontributesignificantlybythemselvesandtheirinteraction1 withbehavioral,occupationalandmetabolicfactors1.
Disability-adjusted life-years attributable to behavioral,environmental, occupational, and metabolic risk factors1.4
FlintWaterCrisis• LeadPoisoninginkids
• Immunedisorders• Criminaltendencies• Behaviorandlearningproblems• LowerIQandhyperactivity• Slowedgrowth• Hearingproblems• Anemia
• Noknownsafelevelofleadinachild'sblood.• LeadActionLevel:10%ofdrinkingwater>10partsperbillion.• CDC’spublichealthactions:whenthelevelofleadinachild’sblood≥5microgramsperdeciliter2.
http://electrochemistryresources.com/wp-content/uploads/2016/02/corrosion-water-pipe.jpg
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Exposome3-6
• Encompasseslife-courseofenvironmentalexposures(includinglifestylefactors)fromprenatalperiodonwards.• Complementsgenomebyprovidingacomprehensivedescriptionoflifelongexposurehistory.
GeneralExternalEnvironment
SpecificExternal
EnvironmentInternalEnvironment
Overlappingdomainswithinexposome6
Exposomics
• Studyofdefining,generatingandutilizingexposomes inbiomedicalresearch.• Ongoingefforts:
• HELIX7:Earlylifeexposome• EXPOsOMICS8:Assessexposures• HEALS9:Studiesexposuretoenvironmentalstressorsandhealthoutcomes• NIH’sEnvironmentalinfluencesonChildHealthOutcomes(ECHO)Program10:Understandingtheeffectsofenvironmentalexposuresonchildhealthanddevelopment
• Requiresasystemsbiologyapproach.• ‘Expotying’:Exposureofabiologicalentityusuallywithreferencetoaspecificcharacteristicunderconsideration.• AlsocalledasExposomeInformatics,ExposureInformationScience.• ProvidesgreatopportunitiestoBiomedicalInformatics11.
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DefiningandGeneratinganAirQualityExposome
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Background• AirQuality(AQ)hasbeenassociatedwithvariousadversehealtheffects
• Asthma• Cardiovasculardisease• Respiratoryinfections• Cancers• Impairedglucosetoleranceduringpregnancies12-15.
• ResearchersattheUniversityofUtahareembarkingonclinicalstudiestounderstandassociationsbetweenthepeculiarAQpatternsinSaltLakeCityandclinicalconditions:• Cerebralvenousthrombosis• Exacerbationsofidiopathicpulmonaryfibrosis• Suicide• Reproductiveoutcomes• Cancers.
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SaltLakeCityAirQuality
• Pronetowinterinversionswherecoldersurfacetemperaturestrapfineparticulatematter(PM2.5)whichposesserioushealthconcerns.• Summermonthsinthevalleyhaveincreasedozone(O3)levels16.• Natural/Quasi-experimentalconditions.
Courtesy:Dr.K.Kelly
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OpenFurther17-18
• QueryTool• FederatedQueryEngine
• DataSourceAdapters• Admin&SecurityComponents
• VirtualIdentityResolutionontheGO(VIRGO)
• Quality&AnalyticsFramework
• MetadataRepository• Terminology/OntologyServer
• AirQualityModellingUnit
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OpenFurtherDeploymentsandUses
CohortSelection,UniversityofUtah
ComparativeEffectivenessResearch,
PHIS+
CohortSelection,UniversityofNorth
Carolina
DataIntegration&AnalyticsPipeline,UtahDepartmentofHealth
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AirQuality- ClinicalDataFederation
• DemonstratedfeasibilityoffederatingairqualitydatafromEnvironmentalProtectionAgency(EPA)withclinicaldatafromUniversityofUtahusingOpenFurther19-20.• AbilitytoselectdifferentcohortsofpatientslivinginSLCcountyandhavingclinicalconditions(e.g.asthma)occurrencesthatwererelatedtotemporalvariationsofairpollutantconcentration.
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AirQualityMonitoringinSaltLakeCounty
• ThreemonitoringstationsinSaltLakeCounty.• AQspeciesconcentrationvariationsduetotopography,altitudeandmeteorology21-22
• Whatistheairqualityatanyotherlocation?• Needforcross-linkingpatientlocationsandconditionoccurrences:HighResolutionSpatio-temporalAirQualityGrid
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AirQualityExposome
Pollutant&Quantity
Travel Home Ventilation
Outdoors ClinicalConditions
BiologicalMembranes
Others
AirQuality Socio-economic Behavioral Clinical/Physiological Genomic Proteomic
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BiomedicalResearchAirQualityRequirements
• Primaryneed:understandrisksassociatedwithbeingexposedwithvariousairpollutants.• Manifestationsfollowingexposurecouldoccur• Immediately• Afteralagphase• Couldpersistoverlongdurations.
• Needforunderstandingpathophysiologyandmechanismsofthesemanifestations.• Currentresearchmainlyassociatessinglepollutantandclinicalconditions,futureareasofresearchcouldincludeexposurestomultiplepollutants.
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UtilizingAirQualityDatainBiomedicalResearch
• IntegratingAQandbiomedicaldataneedstosupport• Spatio-temporalvariationsofairpollutantspecies.• Heterogeneousdata.• Locationofindividuals.• Timingoftheoccurrenceofevents.
• AQdataandresearchrequirementgranularitiesvaryfrominstantaneoustolongerdurationaveragesdepending.• SimplificationofunderstandingandintegratingAQdatawithbiomedicaldata.• Supportbench,translational,clinicalandpopulationresearch.
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ChallengesandInformaticsMethodsandSolutions
DataSources
MathematicalModeling
UncertaintyCharacterization
DataIntegration• Semantics• Metadata• Time&eventmodeling• Infrastructureformulti-scale,multi-omicsintegration
Presentation/Visualization• Salientfeatureextraction
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UniversityofUtah’sPRISMSInformaticsInfrastructure
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PediatricResearchusingIntegratedSensorMonitoringSystems(PRISMS)• Sensor-based,integratedhealthmonitoringsystemsformeasuringenvironmental,physiological,andbehavioralfactorsinpediatricepidemiologicalstudiesofasthma,andeventuallyotherchronicdiseases23.• SensorDevelopmentProjects• InformaticsPlatformTechnologies• DataandSoftwareCoordinationandIntegrationCenter
• UtahTeam:ElectricEngineering,ChemicalEngineering,ComputerScience,AtmosphericSciences,IndustrialEngineering,Informatics,SoftwareDevelopers,Nursing,Pediatrics.
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AirQualityDataSources
Differentairqualityspecies• ParticularMatter:PM2.5,PM10,UPF• Ozone• CarbonMonoxide• NOx (nitricoxideandnitrogendioxide)• Sulphur Dioxide• Lead• WaterVapor• CarbonDioxide• VolatileOrganicCompounds
Choiceofselectablesourcesforeachspecies
Highresolutionspatio-temporalAQgrid• Personalization
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TypesofAirQualitySources
PersonalSensors LaserCeilometers NovelSensors MobileSensors
BalloonSensors Satellite-derivedaerosolopticaldepth
measurements
StateEnvironmentalDepartmentNetworks
EnvironmentalProtectionAgency
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AirQualityMathematicalModels
• Validatedontheeastcoast• Doesn’tconsiderAltitude• 12kilometerresolution• HierarchicalBayesianmodel
EnvironmentalProtectionAgency–CenterforDiseaseControlModel24
• Describeregionalandsmall-scalespatialandtemporalgradients• UsesmeasuredPMconcentrations,monitoringsitelocation,GIS-basedlocation-specificcharacteristicsandlocation-andmonth-specificmeteorologicaldata,andspatialsmoothingofmonthlyandlong-termaverages
GeneralizedAdditiveMixedModels25
• Fillgapsinmeasureddatawithmathematicalmodels.• AlibraryofAQdatamodelstoprovidehighspatio-temporalresolutionwithaframeworkvalidatethemodeloutput.
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UncertaintyCharacterization26
• SelectionofappropriateAQsourcesandmodels• Inherent:Variationsinunknownconditions• Reducible:Associatedwiththemodelandinputconditions.• ExposureUncertainty:Arisingduetodifferencesinperson’sexposureandtrueambientAQlevels. 24
OpenFurtherModifications1 2 3
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SemanticsforDataIntegration• SemanticinteroperabilityforInternetofThings(IoT)27
• StoredinTerminology/OntologyServer• Examples
• Describessensorsandobservations,andrelatedconcepts.SemanticSensorNetworkOntology28
• StandardmodelsandXMLschemafordescribingsensorssystemsandprocessesassociatedwithsensorobservations.
SensorModelLanguage(SensorML)29
• Standardmeasuresrelatedtocomplexdiseases,phenotypictraitsandenvironmentalexposures.PhenX PhenotypicTerms30
• Facilitatecentralizationandintegrationofexposuredatatoinformunderstandingofenvironmentalhealth.Exposureontology(ExO)31
• GeneOntology,UniProt,SNOMEDetc.Standardbiomedicalontologiesandterminologies 26
Metadata
• StoredinMetadataRepository18
• Relationalorgraphstores• Stores• SourceandCentralDataModels
• Harmonizedsensordatamodel• Dataprovenanceandassociateduncertainty• Inter-modeltransformativefunctions
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Time&Events
• Datamodeledandstoredinprimitiveformonatimelineasevents.• Transformedtohigher/analyticalmodelsbasedonuse-cases.• Timemodeledas32:• Unbounded:Containsupperand/orlowerboundswithrespecttoitsorderrelationship.• Dense:aninfinitesetofsmallerunits.• Discrete:everyelementhasbothanimmediatesuccessorandanimmediatepredecessor,ifunbounded,andwithintheboundsifbounded.• Instants&Intervals(upperandlowertimepoints).• Finestgranularityavailablewiththesource.
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DataIntegrationWorkflow
1. Usercanqueryforacohortorcompletedatasets.2. (a&b)Heterogeneousdatasources(whereAandB
representmobile sensordatasources,CrepresentenvironmentalmonitoringdatasourcesandDrepresentbiomedical datasources),and(ifneeded)mathematicalmodelsusingEDMUareselected.• Environmentaldata(A,B&C)harmonizedtothecentral
modelsstoredinMDR.Selectionofmathematical modelsmanagedintheEDMU.
3. OFsynthesizeresultsindifferentanalytical models.4. Presentsthemascohortsand/oraggregatedresults. 29
ResearchUse-Cases
Retrospective&Population
Studies
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Conclusion
• Scalableinformaticsarchitecturethatisgeneralizablebeyondairqualityandpediatricasthma.• Integratesmulti-scaleandmulti-omicsdata.• Genome-phenome-exposome
• BigDataintegration:volume,velocity,variety,veracityforresearchvalue.• Robustpipelineforresearchdatadeliverywithdecisionsupport.• Supportdifferenttypesofresearch.
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