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.

Questions&Answers

bustun@ku.edu.tr

@ustunb

bedirhan-ustun