medical decision sciencesinformatics.snubi.org/2004fall/medicaldecisionsciences.pdf ·...

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Medical Decision Sciences Medical Decision Sciences

Ju Han Kim, M.D., Ph.D., M.S.Ju Han Kim, M.D., Ph.D., M.S.SNUBI: SNUBI: SNUBiomedicalSNUBiomedical InformaticsInformatics

Medical Decision SciencesMedical Decision Sciences

•• Medical decision makingMedical decision making•• Data, Information, & KnowledgeData, Information, & Knowledge•• Knowledge representationKnowledge representation•• BayesBayes rulerule•• ROC curveROC curve•• Clinical decision analysisClinical decision analysis•• Machine learningMachine learning•• Clinical decision support systemClinical decision support system•• Grade CGrade C

Clinical practice as decision makingClinical practice as decision making

•• The The ability to make good decisionsability to make good decisions is the is the hallmark of the good performing hallmark of the good performing professionalprofessional..

•• Information systemInformation system can be used for good can be used for good decision makingdecision making and and decision supportdecision support..

•• Therefore, it is important to understand the Therefore, it is important to understand the theory of decision makingtheory of decision making..

A working definition of a decisionA working definition of a decision

An irrevocable allocation of resourcesAn irrevocable allocation of resources

•• Resources can be time, money, attention Resources can be time, money, attention cycles, cycles, emotional energy, or anything else emotional energy, or anything else of of valuevalue to the decision maker.to the decision maker.

•• IrrevocableIrrevocable allocation is required, because allocation is required, because otherwise there is no crisp way do define otherwise there is no crisp way do define when a decision should be made.when a decision should be made.

Decision is different than worryingDecision is different than worrying

Worrying typically does not involve Worrying typically does not involve allocation of resources.allocation of resources.



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Data, Information, & Knowledge Data, Information, & Knowledge

•• DataData

•• InformationInformation

•• KnowledgeKnowledge

Informatics: Informatics: knowledge about knowledge

1.1. Combining several simple ideasCombining several simple ideas into one compound one, into one compound one, and the all and the all complex ideascomplex ideas are made. are made.

2.2. The second is The second is bringing two ideasbringing two ideas, whether simple or , whether simple or complex, together, and setting them by one another so as complex, together, and setting them by one another so as to take a view of them at once, without uniting them into to take a view of them at once, without uniting them into one, by which it gets all its ideas of one, by which it gets all its ideas of relationsrelations. .

3.3. The third is The third is separating them from all other ideasseparating them from all other ideas that that accompany them in their real existence: this is called accompany them in their real existence: this is called abstractionabstraction, and thus all its , and thus all its general ideasgeneral ideas are made.are made.

John Locke, John Locke, An Essay Concerning Human UnderstandingAn Essay Concerning Human Understanding (1690)(1690)

The acts of the mind wherein it exerts its power over The acts of the mind wherein it exerts its power over simple ideassimple ideas, are chiefly these three: , are chiefly these three:



•• Binding Binding

•• CombiningCombining

•• RelatingRelating

•• AbstractingAbstracting

Data, Data Structure, Abstraction, RepresentationData, Data Structure, Abstraction, Representation

FROG

2

BIRD

HUNTER BIRDMAN

MAN BIRDHUNTS

Grumpy Happylikes

Manager

Is_a

Grumpy Happylikes

Manager

Is_a

Managers

Grumpy

Is_a

likes

Happy

Happy

Data, Data Structure, Abstraction, Data, Data Structure, Abstraction, Knowledge Representation, & FormalismKnowledge Representation, & Formalism

Frames: an exampleFrames: an exampleNAME : Acute glomerulonephritisTriggered by

Confirmed by

Caused byCauses

Complications

Differential diagnosis

facial edema, not painful, not erythematical, symmetrical, etc.malaise, asthenia, anorexia, etc.

recent streptococci infectionsodium retention, acute hypertension, nephroticsyndrome, etc.acute kidney failure

(If chronic high blood pressure then chronicglomerulonephritis)(If recurrent edema then nephrotic syndrome)

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Semantic Network & Medical OntologySemantic Network & Medical Ontology

생물학적 기능

병리학적 기능

질병 또는 증상

is_a

is_a

is_ais_a

결핵 저칼륨혈증

Semantic Network : UMLSSemantic Network : UMLSOrganism

Finding

Laboratoryor test result

Biologicfunction

Bodysystem

Injury orpoisoning

Anatomical structure

Organismattribute

Sign orsymptoms

Embryonicstructure

Fully formedanatomicalstructure

Organismfunction

Cell or molecular

dysfunction

Molecularfunction

Cellfunction

Organ or tissue

function

Mentaldysfunction

Experimentalmodel ofdisease

Disease orsyndrome

Physiologicfunction

Pathologicfunction

Plant Virus Animal

Vertebrate

Mammal

Human

Mentaldysfunction

Bodypart

part of

disrupts

evaluation of

prop of

disrupts

conceptual part of

process of

evaluation of

conceptual part of

Medical Language & Classification SystemsMedical Language & Classification Systems•• ICDICD•• MeSHMeSH•• CPT 4 CPT 4 •• SNOMEDSNOMED•• HLHL--77•• LOINCLOINC•• Arden SyntaxArden Syntax•• UMLSUMLS

MetathesaurusMetathesaurusSemantic NetworkSemantic NetworkInformation Source MapInformation Source Map

•• Controlled Vocabularies and Vocabulary ServerControlled Vocabularies and Vocabulary ServerPTXT / COSTAR / TMR / RMRSPTXT / COSTAR / TMR / RMRSMED (Medical Entities Dictionary)MED (Medical Entities Dictionary)PEN & PADPEN & PADGALENGALEN



ProbabilityProbability

)()|()()|()()|(

)()()|()|(

)()|()()|()(/)()|(

)()()()(

1)(1)(0

DpDSpDpDSpDpDSp

SpDpDSpSDp

ApABpBpBApBpBApBAp

BApBpApBApApAp

i

⋅+⋅⋅

=⋅

=

⋅=⋅∴∧=

∧−+=∨

=

≤≤

∑AA BB

Bayes Bayes Rule Rule

)|()|(

)()(

)|()|(

)()(

)(1)(

DSpDSp

DpDp

SDpSDp

ApAp

ApApOdds

•=

=−

=

•• oddsodds--likelihood ration form of likelihood ration form of Bayes Bayes formulaformula

ProbProb = 1 / 3= 1 / 3Odds = 1 / 2 =0.5 Odds = 1 / 2 =0.5

AA

Post. Odds = prior odds x likelihood ratioPost. Odds = prior odds x likelihood ratio

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Diagnostic test characteristics Diagnostic test characteristics

D +D + D D ––T + aT + a bb a+ba+bT T -- cc dd c+dc+d

a+ca+c b+db+d a+b+c+da+b+c+d

•• Sensitivity = a / (a+c) = p(T+|D+)Sensitivity = a / (a+c) = p(T+|D+)•• Specificity = d / (b+d) = p(TSpecificity = d / (b+d) = p(T--|D|D--))•• Positive Predictive Value = a / (a+b) = p(D+|T+)Positive Predictive Value = a / (a+b) = p(D+|T+)•• Negative Predictive Value = d / (c+d) = p(DNegative Predictive Value = d / (c+d) = p(D--|T|T--))


예제예제)) Sensitivity=99.99%, specificity=99.9% Sensitivity=99.99%, specificity=99.9% 인인 최신의최신의

에이즈에이즈 검사가검사가 개발되었다개발되었다. . 철수는철수는 이이 검사에검사에 양성반응을양성반응을

보였다보였다. . 철수가철수가 에이즈에에이즈에 감염됬을감염됬을 확률은확률은 얼마인가얼마인가? ? ((현재현재 한국인의한국인의 에이즈에이즈 유병율은유병율은 0.00010.0001이라고이라고 한다한다.).)

1.1. 99%99%2.2. 95%95%3.3. 80%80%4.4. 50%50%5.5. 10%10%


AIDS AIDS no AIDSno AIDST +T +T T --

9,9999,99911

10,00010,000

•• Sensitivity=99.99%Sensitivity=99.99%•• Specificity=99.9%Specificity=99.9%

11999 999

10001000

Prevalence=0.0001Prevalence=0.0001

00,00000,00000,000 00,000 00,00000,000

109,999109,99999,900,00199,900,001

100,010,000100,010,000

•• Positive predictive value = 9,999 / 109,999 < 10%Positive predictive value = 9,999 / 109,999 < 10%•• Negative predictive value = 1.0Negative predictive value = 1.0


T+ (TP)T+ (TP) TT-- (FN)(FN)

T+ (FP)T+ (FP) TT-- (TN)(TN)

T+ (TP)T+ (TP) TT-- (FN)(FN)

T+ (FP)T+ (FP) TT-- (TN)(TN)



ROC CurveROC Curve

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Why decision making is so hard?Why decision making is so hard?

UNCERTAINTY!!UNCERTAINTY!!

•• If you have all information about the If you have all information about the probabilitiesprobabilities of different events, then it of different events, then it typically is NOT a hard decisiontypically is NOT a hard decision

•• If there is no uncertainty, and the decision If there is no uncertainty, and the decision is still hard, then the problem is that the is still hard, then the problem is that the utility functionutility function is not clearis not clear

Decision TheoryDecision TheoryA theory for decision making that is A theory for decision making that is rationalrational..

Expected value decision makingExpected value decision makingMaximize expected valueMaximize expected value among the among the decision alternativesdecision alternatives

Clinical Decision AnalysisClinical Decision Analysis

Chance node

Decision node

Transition probability

Active transition

Alternating chance & decision nodesAlternating chance & decision nodes

사망

Simple decision treeSimple decision tree

즉각적족부절단

관찰

생존

사망

반흔형성

병변확장

회복

사망

무릎위

절단

Result

사망

무릎아래절단

무릎위절단

생존

A trauma caseA trauma case



관찰

생존 (0.99)

사망 (0.01)

반흔형성 (0.7)

병변확장 (0.3)

회복

사망

무릎위

절단

Result

사망

무릎아래절단

무릎위절단사망 (0.1)

생존 (0.9)

PROBABILITY: PROBABILITY: objectiveobjective

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관찰

생존 (0.99)

사망 (0.01)

반흔형성 (0.7)

병변확장 (0.3)

0.00

1.00 회복

0.95

사망

무릎위

절단

0.98

0.00

Result

사망

무릎아래절단

Utility


생존 (0.9)

UTILITY: UTILITY: subjectivesubjective



관찰

생존 (0.99)

사망 (0.01)

반흔형성 (0.7)

병변확장 (0.3)

0.00

1.00 회복

0.95

사망

무릎위

절단

0.98

0.00

Result

사망

무릎아래절단

Utility


생존 (0.9)

EXPECTED VALUEEXPECTED VALUE

0.970

0.956

0.855

0.98 X 0.99 = 0.970

0.95 X 0.9 = 0.855

0.7 X 1.0 + 0.855 X 0.3 = 0.956

Sensitivity analysisSensitivity analysis


관찰

생존 (0.99)

사망 (0.01)

반흔형성 (0.7)

병변확장 (0.3)

1.00


생존 (0.9)

0.970

0.956

0.855

.5

.6

.7

.8

.91.0

0.8

반흔형성

What if you donWhat if you don’’t know the numbers?t know the numbers? Standard gambleStandard gamble

Utility is not PROBABILITYUtility is not PROBABILITY

Immediate deathImmediate deathon surgery on surgery

Perfect visionPerfect vision

blindnessblindness pp

11--pp

Getting utilityGetting utility

Risk seeking vs. risk averseRisk seeking vs. risk averse Embedded Markov chainEmbedded Markov chain

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•• Medical decision makingMedical decision making•• Data, Information, & KnowledgeData, Information, & Knowledge•• Knowledge representationKnowledge representation•• BayesBayes rulerule•• ROC curveROC curve•• Clinical decision analysisClinical decision analysis•• Machine learning: Artificial Intelligence in MedicineMachine learning: Artificial Intelligence in Medicine•• Clinical decision support systemClinical decision support system•• Grade CGrade C

Machine Learning Approach in Bioinformatics

Classifications

Non-exclusive Exclusive

Supervised Unsupervised

Hierarchical Partitional

Artificial Intelligence in MedicineArtificial Intelligence in Medicine

•• RuleRule--based system: MYCINbased system: MYCIN•• Classification treeClassification tree•• Bayes Bayes netnet•• Artificial neural netArtificial neural net•• Rough setRough set•• Reinforcement learningReinforcement learning

RuleRule--based systembased systemMYCINMYCIN

>> What is the patients name? John Doe.

>> Male or Female? Male.

>> Age? 55.

>> Let's call the most recent positive culture C1. From what site was C1 taken?

From the blood. ... >> My recommendation is as follows: give gentamycinusing a dose of 119 mg (1.7 mg/kg) q8h IV [or IM] for 10 days. Modify dose in renal failure. Also, giveclindamycin using a dose of 595 mg (8.5 mg/kg) q6h IV [or IM] for 14 days.

MYCINMYCIN

IF: 미생물이 그림 양성으로 염색된다.

미생물이 형태학적으로 막대균이다.

환자가 감염되었을 위험이 높다.

THEN: 감염체는 슈도모나스로 추정된다. (확신도 = 0.6)

RuleRule--based systembased system

•• RulesRulesA A →→ BBB and C B and C →→ DDD and E D and E →→ GG

•• Backward chainingBackward chaining

•• Forward chainingForward chaining

A B

D

G

E

C

MYCINMYCIN

RuleRule--based systembased system

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Classification TreeClassification TreeSunburn at the BeachSunburn at the Beach

Classification TreeClassification Tree

Classification TreeClassification Tree Classification TreeClassification Tree

Classification TreeClassification Tree Classification TreeClassification Tree

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Bayesian belief netBayesian belief net Artificial Neural NetworkArtificial Neural NetworkA Universal Function A Universal Function ApproximatorApproximator

Reinforcement LearningReinforcement Learning

Markov Chains and Hidden Markov Model Markov Chains and Hidden Markov Model Medical Decision SciencesMedical Decision Sciences


“Our integrated approach to medicine skillfully combines an array of holistic alternative treatments with a sophisticated computerized billing services”

Clinical Information System

• OCS - LIS - RIS - PACS - EMR

• Departmental Information Systems

• Library / Factual Information Systems

• Research Database

Hospital Information System is Dead!

Intelligent Integration of

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Decision support systemDecision support system

•• Passive systemPassive systemConsultant systemConsultant systemCritiquing systemCritiquing system

•• SemiSemi--active system active system ReminderReminderAlertAlert

•• Active systemActive system

Your answeris...

X >1

Sex=F

UTI314:

Y > 1

Hello!...

k>1Go 314

Go 1200

Go 354

Hx of hos..

adolescentSub R34

freqency

urgency pain

Age > 30

For your..

Mx. We are...

Allergy?

Hx.?

Admin?

Help?

UTI:RTS Protocol 3110

Go 399Sub R34

Sub R44

Sub R55

Go 1200

Flow chart systemFlow chart system

Flow chart systemFlow chart system•• simple, easy to buildsimple, easy to build•• hard to manage / maintainhard to manage / maintain

Diagnostic strategy

Decision support systemDecision support system

Knowledge baseFactual

Information

Inference Engine

•• flowchartflowchart•• rulerule--based systembased system•• frameframe--based systembased system•• sequential sequential BayesBayes•• modelmodel--based system based system

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Decision support componentsDecision support components

•• GLIF (Guide Line Interchange Format)GLIF (Guide Line Interchange Format)•• Arden syntaxArden syntax•• MLM (Medical Logic Module)MLM (Medical Logic Module)•• OneOne--rule expert systemrule expert system

GLIF : GLIF : GuideLineGuideLine Interchange FormatInterchange Format

Data modelData model

GLIF : GLIF : GuideLineGuideLine Interchange FormatInterchange Formatimplementationimplementation Medical Decision SciencesMedical Decision Sciences


Theory and PracticeTheory and Practice

In theory, theory and practice In theory, theory and practice should be the same. should be the same.

But in practice, theory and practice But in practice, theory and practice are different.are different.

Grade CGrade C

NEJM 1994NEJM 1994

medical decision sciencesinformatics.snubi.org/2004fall/medicaldecisionsciences.pdf ·...

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