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Impact ofBig DataonGlobalHealth and Clinical Decision Making
Professor Dr.BedirhanUstun
4V‘sofBigData
Volume• Dataquantity
Velocity• DataSpeed
Variety• DataTypes
Veracity• Messiness
Security
Smarter Healthcare Multi-channel
sales
Telecom
Manufacturing
Traffic Control Trading Analytics
SearchQuality
EverybreathyoutakeEverymoveyoumakeEverybondyoubreakEverystepyoutakeI'llbewatchingyou
Byte
Byte : one grain of rice
Kilobyte
Byte : one grain of riceKilobyte : cup of rice
Megabyte
Byte : one grain of riceKilobyte : cup of riceMegabyte : 8 bags of rice
Gigabyte
Byte : one grain of riceKilobyte : cup of riceMegabyte : 8 bags of riceGigabyte : 3 Semi trucks
Terabyte
Byte : one grain of riceKilobyte : cup of riceMegabyte : 8 bags of riceGigabyte : 3 Semi trucksTerabyte : 2 Container Ships
Petabyte
Byte : one grain of riceKilobyte : cup of riceMegabyte : 8 bags of riceGigabyte : 3 Semi trucksTerabyte : 2 Container ShipsPetabyte : Blankets Manhattan
One Byte Exabyte
Byte : one grain of riceKilobyte : cup of riceMegabyte : 8 bags of riceGigabyte : 3 Semi trucksTerabyte : 2 Container ShipsPetabyte : Blankets ManhattanExabyte : Blankets west coast states
Byte : one grain of riceKilobyte : cup of riceMegabyte : 8 bags of riceGigabyte : 3 Semi trucksTerabyte : 2 Container ShipsPetabyte : Blankets ManhattanExabyt : Blankets west coast statesZettabyte : Fills the Pacific Ocean
Zettabyte
Byte : one grain of riceKilobyte : cup of riceMegabyte : 8 bags of riceGigabyte : 3 Semi trucksTerabyte : 2 Container ShipsPetabyte : Blankets ManhattanExabyte : Blankets west coast statesZettabyte : Fills the Pacific OceanYottabyte : An EARTH SIZE RICE BALL! Yottabyte
Byte : one grain of riceKilobyte : cup of riceMegabyte : 8 bags of riceGigabyte : 3 Semi trucksTerabyte : 2 Container ShipsPetabyte : Blankets ManhattanExabyte : Blankets west coast statesZettabyte : Fills the Pacific OceanYottabyte : A EARTH SIZE RICE BALL!
90%oftoday’sstoreddatawasgeneratedinjustthelasttwoyears.
Datageneration
MobileDevices
Readers/Scanners
Sciencefacilities
Microphones
Cameras
SocialMedia
Programs/Software
Market Size
Source:Wikibon Taming BigData
Europeprecisionmedicinemarketsize,byapplication,2013- 2023(USDMillion)
BigDataUseCases
BeyondtheHype… Hope ?
• BigDataisnot aFAD• YOUarealready usingit…• Itisheretostay• BigDatahasMinimalStructure
• BigDataIsusuallyRawData• ItisNOT likeatypicalRelationalDatabase
• BigDataisavailable - andLessExpensive• BigDataisnotcollectedforapurpose- hasnomap• Itisyourbusiness– yourtimeandmoneyisatwork
EndofPart1
Avoidingane-Tower of Babel onBigData
Millionsoftypesofdata- nolinkage
Linkingdatafromdifferentsources:xAPIs
CAPTURINGMEDICALDATA
GenealogyofICDà 1664
353years
38
ReportingofMortalityintheWorld
Information Paradox
0
100000000
200000000
300000000
400000000
500000000
600000000
700000000
800000000
1 2 3 4
YLLs
VR countries vs No VR
Burden of Mortality
1.hasBIGHOLES
WhyisthisSooooo important?
UK experience:16billionpounds and…
2. Doesnottalktoeachother
GIGO:GarbageIn
Out?
theinformationYOU-
₋ have isnot whatyouwant
₋want isnot whatyouneed
₋ need isnot whatyoucanhave
Finagle's LawofInformation
Computers areSTUPID
?Theycannotaskquestions
¿ Theymay– onlyifyouenablethem-
giveyouanswers.
PabloPicasso
KnowledgeRepresentationthe triad of things, thoughts and words(Ogden&Richards,1923)
APPLETERM
Ontology (philosophy)theOrganizationofRealityJ !!!
ü Ontology(computerscience)– theexplicit– operationaldescriptionof
theconceptualizationofadomain• Entities• Atributes• Values
• Anontologydefines:– acommonvocabulary– asharedunderstanding/exchange:
• amongpeople• amongsoftwareagents• betweenpeopleandsoftware
– toreusedata- information– tointroducestandardstoallow
interoperability
Whatis“NOntology”?
PlacingWHOClassificationsinHIS&IT
PopulationHealth• Births• Deaths• Diseases• Disability• Riskfactors
e-HealthRecordSystems
ICD
ICF
ICHI
Classifications
KRs
Terminologies
Clinical• DecisionSupport• Integrationofcare• Outcome• Safety
Administration• Scheduling• Resources• Billing
Reporting• Cost• Needs• Outcome
THECONTENTMODELAnyCategoryinICDisrepresentedby:
1.ICDConceptTitle1.1.FullySpecifiedName
2. ClassificationProperties2.1.Parents2.2Type2.3.UseandLinearization(s)
3.TextualDefinition(s)
4.Terms4.1.BaseIndexTerms4.2.InclusionTerms4.3.Exclusions
5.BodyStructureDescription5.1.BodySystem(s)5.2.BodyPart(s)[AnatomicalSite(s)]5.3.MorphologicalProperties
6.ManifestationProperties6.1.Signs&Symptoms6.2.Investigationfindings
7.CausalProperties7.1.EtiologyType7.2.CausalProperties- Agents7.3.CausalProperties- CausalMechanisms7.4.GenomicLinkages7.5.RiskFactors
8.TemporalProperties8.1.AgeofOccurrence&Occurrence Frequency8.2.DevelopmentCourse/Stage
9.SeverityofSubtypesProperties
10.FunctioningProperties10.1.ImpactonActivitiesandParticipation10.2.Contextualfactors10.3.Bodyfunctions
11.SpecificConditionProperties11.1BiologicalSex11.2.Life-CycleProperties
12. TreatmentProperties
13.DiagnosticCriteria
BigDataOrganizationZoom-inZoom-Out
Summery:Useontologytobridgedatasetsacrossdomains• Basictechnology
• Terms(classes/instances)definedinontologyareusedascommonvocabulary forsearchdata.
• IftheontologyhasmappingtoMultipleDBs,theusercansearchacrossthem.
• MotivationandIssue• CombinationsofmultipledatasetscouldbevaluableforBigDataAnalysis.• However,togetallcombinationsacrossmultipleBigDataisnotrealisticfortheirsize.
• Requestsbytheusersareverydifferentaccordingtotheirinterests.
• OntologyEngineeringforBigDatatoSolvetheissue• OntologyExplorationcontributetoobtainmeaningfulcombinations(=viewpoints)accordingto theusers’interests.
3. NeedsBig Intell igence
Knowledge
INPUTS
Big Data Science
OUTPUT
• Mechanisms
• Interventions
• Policies
• Statistics
• Aggregation
• Ontologies
• Data
• Information
ComputationalProcessing
RewritingICD using {SNOMED}exampleofDepressiveDisorderF32.0
A. Lowmood {41006004}
Lossofinterest {417523004 }
Lowenergy {248274002}
1. Appetite (decrease,increase) {64379006, 72405004}
2. Bodyweight (decrease,increase) {89362005, 8943002}
3. Sleep (decrease,increase){59050008, 77692006}
4. Psychomotor (decrease,increase){398991009, 47295007}
5. Libidoloss {8357008}
6. Lowselfesteem {286647002, 162220005}
7. Guilt,selfblame {7571003} 8. Thoughtsofdeath…
9. SuicideIdeation {102911000, 6471006}
B.
Grade 3 hypertension
Grade 2 hypertension
Grade 1 hypertension
Highnormal
normal
optimal
120 130 140 150 160 170 180
Systolicpressure
Diastolicpressure
172
102
110
105
100
95
90
85
80
KnowledgeRepresentation
62
Real Time Public HealthRule-based Aggregation @ Individual, Facility, Population levels
Public Health, Epi & Surveillance
Findings InterventionsEvents
Clinical Information
ReimbursementResource Management
BeyondSemanticInteroperabilityforHIS
• SearchusingConceptsaboveWords• HowmanypatientsdohavediabetesmellitustypeII?
• ExtractionofConcepts fromHealthRecords• AutomatedextractionofHbA1cresults ofselectedpatientswithDMtypeIIfromlabreportswithinlastyear
• StatisticalIndexonCommunity Collections• Calculationofcoveragegap fortreatmentneedfordiabetesmellitus
• ConceptNavigation acrossCollections• ComparisonofregionA withregionB etc
4. needsUSER Tools
Clinical Use Case: Exploration of Cough
Fever
386661006
COUGH
49727002
WET COUGHsputum
28743005
HemoptisiaBlood in Sputum
207069003
• X-ray : Tbc? • Culture
399208008
104184002
• Diagnosis: Tuberculosis 154283005A 15.0
• Treatment: DOTs { 324453004 }
ALGORITHMS
From www.research.vt.edu/.../images/Asymmetry.jpg
Informationasymmetry in HEALTH CARE
GARBAGE IN:GOLD OUT?
• …recognition?• …diagnosis?• …accuracyofdiagnosis?• …treatment?
— prescription?— compliance?
•…outcomes?• …patientsatisfaction?• … patientsafety?
IsHealth lessvaluablethanStockExchange?
- WhatdoyouthinkofBIGDATA?
- Ithinkitwouldbe agoodidea.