computable semantics and probabilistic graphical models where probabilistic systems and semantics...

Computable Semantics and Probabilistic Graphical Models

Where Probabilistic Systems and Semantics Rub Elbows

Peter Haug, MDHomer Warner Center for Informatics ResearchIntermountain Healthcare

First of all: Thanks

This work has many contributers:

Dominik Aronsky, MD, PhD

Jeffrey Ferraro, PhD

Stan Huff, MD

Scott Evans, PhD

Robert Hausam, MD

Lee Pierce

Xinzu Wu, PhD

Matthew Ebert

Kumar Mynam

And many more!

Please ask questions …3

Agenda• Why Decision Support?• Introduction: Bayesian Diagnostic Networks

• Bayesian Systems• A Framework for Computable Models

• A Few Bayesian Tools• Diagnostic Systems

• Representing the Semantics of Diagnosis• Diagnostic Modeling with Ontologies• Ontologies -> Bayesian Network

• Clinical Data• A Brief Look at Medical Data Forms

‘... man is not perfectible. There are limits to man’s capabilities as an information processor that assure the occurrence of random errors in his activities.’~ Clement J. McDonald, MD (1976)

‘The complexity of modern medicine exceeds the inherent limitations of the unaided human mind.’~ David M. Eddy, MD, Ph.D. (1990)

Computerized Decision Support:Core Assumptions

Patient

Underlying principle:

We are designing the system so that the

computer is an active part of patient

care, not just a way of getting data to

people to read.

The Reverend Thomas Bayes

Bayes set out his theory of probability in 1764. At that time, Richard Price, a friend of Bayes, discovered two unpublished essays among Bayes's papers which he forwarded to the Royal Society.

1702 to 1761

A Way to Think about Probabilistic Systems(and an introduction to some terminology)

Learning from Data• The data comes from Health Care Encounters• It is captured in Electronic Health Records (EHRs)• It is aggregated and organized in Enterprise Data

Warehouses (EDW)• It includes the diagnoses and the data that support

them

Bayesian Networks • Model the joint probability distribution of the data

and diagnoses• Use directed graphs to structure these models

Medical Information System

Episodes of Care

Enterprise Data Warehouse

• Medical Decision Support

• Clinical Research

• Quality Improvement

• Measures of Care

Re-Using Healthcare Data

12

Example: Patients with Symptoms of Heart Disease

Patient Population

Data Collected in a Care Setting

Original Data13

Patient ID

Myocardial Infarction Chest Pain

ST Segment

1 Present Present Elevated2 Absent Absent Normal3 Present Absent Depressed4 Absent Absent Normal5 Absent Absent Normal6 Absent Absent Normal7 Absent Absent Normal

…. …. …. ….

Summarizing the Data: The Numbers

MI No MI

Chest Pain 15 80 95

No Chest Pain 5 900 905

20 980 1000

MI No MI

20 980 1000

A Condensed Look at 1000 Cases

Summarizing the Data: The Numbers

MI No MI

Chest Pain 15 80 95

No Chest Pain 5 900 905

20 980 1000

MI No MI

20 980 1000MI No MI

2% 98% 100%

A Condensed Look at 1000 Cases

Another Summary: The Joint Probability Distribution

MI No MI

Chest Pain 1.5% 8.0% 10%

No Chest Pain 0.5% 90.0% 91%

2% 98% 100%

MI No MI

Chest Pain 1.5% 8.0% 10%

No Chest Pain 0.5% 90.0% 91%

2% 98% 100%

And the “Marginal

Probabilities

Another View of the 2x2 Table16

MI No MI

Chest Pain 75% 8%

No Chest Pain 25% 92%

100% 100%

False Positive Rate: P(F|no D)Sensitivity: P(F|D)

False Negative Rate: P(no F| D) Specificity: P(no F|no D)

Dividing by the Column Marginals

Bayes Equation

)(

)|()()|(

FP

DFPDPFDP

Posterior DiseaseProbability

SensitivityPrior DiseaseProbability

Probability of Finding

Inferring the probability of a Disease (D) from a Finding (F)

Probability Updating

The Disease is Myocardial InfarctionThe Finding is Chest Pain

P(MI) = 2.0% (0.02)P(Chest Pain|MI) = 75% (0.75)P(Chest Pain) = ?

The Question of P(F)

Simple Bayes• Patient has One and Only One

Disease

Multi-Membership Bayes• Patient has Any Group of Disease• Each Disease is Evaluated

Independently

Bayesian Networks• Patient has Any Group of Disease• Diseases are Evaluated According

to Their Collective (Joint) Behavior

)()( ii

DandFPFP

Add All of the Probabilities Of Having Both the Finding and Disease

)|()()( ii

i DFPDPFP

20The Question of P(F)


Disease


Independently



)|()()|()()( iiii DFPDPDFPDPFP

Two States Apply for Each Disease: With and Without the Disease

21The Question of P(F)


Disease


Independently



DiseaseDisease

IntermediateConcept

IntermediateConcept

Finding 1Finding 1 Finding 2Finding 2Finding 3Finding 3Finding 4Finding 4

P(F) is Determined from the Joint Effect of Child Nodes

on Their Parents




Multi-Membership Bayes



?

75.002.0)|(

PainChestMIP

P(MI) = 2.0% (0.02)P(Chest Pain|MI) = 75% (0.75)P(Chest Pain) = 0.02 x 0.75 + 0.98 x 0.08

Using the Multi-Membership Model

08.098.075.002.0

75.002.0)|(

PainChestMIP

Probability Updating24




16.0)|( PainChestMIP

Diagnostic Bayesian Networks(Demonstrating Different Characteristics)

Simple Bayes• Patient has one Disease• All findings are Conditionally Independent

Multi-Membership Bayes• Patient can have multiple Diseases• All Diseases are evaluated independently

Bayesian Networks• Any relationship among diseases and findings• Can represent any of the other models• Multilayered models• Graphical/probabilistic representation of knowledge

Using a Bayesian Network

Examples of Bayesian Diagnostics

In Netica (www.Norsys.com)

Myocardial Infarction

PresentAbsent

2.0098.0

A Simple Bayesian Network(One Finding)

Chest Pain

PresentAbsent

9.3490.7

ST Elevation

PresentAbsent

13.686.4

Troponin Increase

PresentAbsent

4.7495.3

Chest Pain

PresentAbsent

9.3490.7

Myocardial Infarction

PresentAbsent

2.0098.0

A Simple Bayesian Network(Several Findings)

More Diagnostic Examples

(Myocardial Infarction)

Using Pulmonary Diseases • Pneumonia• Asthma• COPD• Pulmonary Embolism

With Increasingly Complex Models• Simple Bayes• Multi-Membership Bayes• Complex Relationships

Bayesian Diagnostic Models(Naïve Bayes)

Fever

PresentAbsent

90.010.0

Elevated_WBC

PresentAbsent

92.08.00

Wheezing

PresentAbsent

10.090.0

Dyspnea

PresentAbsent

15.085.0

Disease

PneumoniaAsthmaChronic BronchitisOther

100 0 0 0

Cough

PresentAbsent

85.015.0

Bayesian Diagnostic Models (Multi-Membership Bayes)

Wheezing

PresentAbsent

10.389.7

Cough

PresentAbsent

8.8891.1

Fever

PresentAbsent

15.184.9

Cough

PresentAbsent

14.585.5

Dyspnea

PresentAbsent

15.384.7

Elevated WBC

PresentAbsent

14.985.1

Dyspnea

PresentAbsent

15.384.7

Pneumonia

PresentAbsent

6.0094.0

Asthma

PresentAbsent

4.0096.0

Bayesian Diagnostic Models(Bayesian Network: Two-Layer)

Elevated WBC

PresentAbsent

15.184.9

Fever

PresentAbsent

15.184.9

Cough

PresentAbsent

12.887.2

Dyspnea

PresentAbsent

15.884.2

Wheezing

PresentAbsent

11.388.7

Asthma

PresentAbsent

4.0096.0

Pneumonia

PresentAbsent

6.0094.0

Bayesian Diagnostic Models(Multi-Layer Bayesian Network)

Wheezing

PresentAbsent

11.388.7

Pneumonia

PresentAbsent

6.0094.0

Asthma

PresentAbsent

4.0096.0

Dyspnea

PresentAbsent

15.884.2

Cough

PresentAbsent

12.887.2

Systemic Inflamation

PresentAbsent

15.184.9

Fever

PresentAbsent

20.179.9

Elevated WBC

PresentAbsent

17.782.3

Wheezing

PresentAbsent

11.388.7

Systemic Inflamation

PresentAbsent

15.184.9

Elevated WBC

0 to 55 to 1010 to 1515 to 2020 to 2525 to 3030 to 3535 to 40

0 +84.915.1.003 0 + 0 + 0 0

8.26 ± 2.3

Temperature

35 to 35.535.5 to 3636 to 36.536.5 to 3737 to 37.537.5 to 3838 to 38.538.5 to 3939 to 39.539.5 to 4040 to 40.540.5 to 4141 to 41.541.5 to 4242 to 42.542.5 to 4343 to 43.543.5 to 4444 to 44.544.5 to 4545

0.100.211.9311.528.628.812.23.332.262.502.472.081.490.960.550.300.180.130.110.10.098

37.9 ± 1.2

Pneumonia

PresentAbsent

6.0094.0

Asthma

PresentAbsent

4.0096.0

Dyspnea

PresentAbsent

15.884.2

Cough

PresentAbsent

12.887.2

Bayesian Diagnostic Models(Multi-Layer with Continuous Variables)

Chest Pain

PresentAbsent

5.9194.1

Dyspnea

PresentAbsent

14.185.9

Cough

PresentAbsent

9.6490.4

Temperature

35 to 35.535.5 to 3636 to 36.536.5 to 3737 to 37.537.5 to 3838 to 38.538.5 to 3939 to 39.539.5 to 4040 to 40.540.5 to 4141 to 41.541.5 to 4242 to 42.542.5 to 4343 to 43.543.5 to 4444 to 44.544.5 to 45

.002 0

8.8718.621.017.612.58.185.233.121.911.170.710.440.270.160.10.062.039.023

37.8 ± 1.2

WBC

0 to 2.52.5 to 55 to 7.57.5 to 1010 to 12.512.5 to 1515 to 17.517.5 to 2020 to 22.522.5 to 2525 to 27.527.5 to 3030 to 32.532.5 to 3535 to 37.537.5 to 40

0 +0.9221.842.524.57.881.950.42.082.015.003 0 + 0 + 0 + 0 + 0 +

9.37 ± 2.6

Pulmonary Embolus

PresentAbsent

2.0098.0

Wheezing

PresentAbsent

8.3291.7

Pneumonia

PresentAbsent

2.0298.0

Chronic Bronchitis

PresentAbsent

0 100

Asthma

PresentAbsent

4.0096.0

Bayesian Diagnostic Models(Multi-Layer with Added Associations)

Using Bayesian Diagnostic Systems in Care

Example: Diagnosing Pneumonia?

Protocols: Computers Intervene in the Workflow(an example from the ED)

Goal:• Rapidly Screen for Pneumonia Patients in the ED• Assess Risk of Death• Apply a Pneumonia Care Protocol

Approach:• Use Probabilistic System to Identify Patients

• Diagnostic Bayesian Networks• Supported with Natural Language Processing*

• Suggest Enrollment in Pneumonia Protocol• Provide Therapeutic Suggestions

*Extracts Data from the X-ray Report

Pneumonia Screening Tool

Data Supporting Pneumonia Assessment Clinical Data

Repository

Pneumonia Protocol

Enrollment

Pneumonia Treatment Protocol

Computable Medical Knowledge Reposotory

Chest Xray Reports

Chest Xray Report Processing

(Structured Data Extraction)

Advanced CDS(Diagnositic Models)

Example: Community-Acquired Pneumonia

Does the patient have pneumonia?

Should we used the protocol?

Apply Pneumonia Care Protocol.

The Emergency Department Workflow

Imbed logic, orders into process of care Imbed logic, orders into process of care

Alerting for Pneumonia in the Patient Tracking System

System Watches the Data Flow in the ED

Identifies Possible Pneumonia Patients

Imbed logic, orders into process of care

Imbed logic, orders into process of care Imbed logic, orders into process of care

Treatment ProtocolUses Data from the EHR Combined with Manually Input Data

Diagnostic System

• Bayesian Network

• Model Trained Using EDW Data

NLP System

• Random Forests-Based Concept Identification

• Trained with Documents in the EDW

Implemented Using:BPDiastolic

< 69.569.5 to 82.5>= 82.5

28.336.235.5

76.9 ± 11

Chloride

< 103.5103.5 to 105.5>= 105.5

42.125.132.9

104.3 ± 1.8

WBC

< 11.8511.85 to 18.75>= 18.75

86.112.41.45

9.46 ± 3.4

PNEUMONIAAbsentPresent

94.95.09

Age

< 15.515.5 to 45.5>= 45.5

8.0645.646.4

42 ± 21

RespRate

< 19.519.5 to 21.521.5 to 27.5>= 27.5

52.324.916.16.72

20.8 ± 3.5

TempC

< 36.7536.75 to 37.4537.45 to 38.05>= 38.05

62.723.86.047.46

36.79 ± 0.63

MeanBP

< 85.585.5 to 99.5>= 99.5

23.035.441.7

95.1 ± 12

BPSystolic

< 121.5121.5 to 148.5>= 148.5

29.444.626.0

134 ± 22

HeartRate

< 85.585.5 to 99.599.5 to 110.5>= 110.5

44.524.713.017.8

92.1 ± 15

Sodium

< 137.5137.5 to 140.5>= 140.5

25.741.832.6

139.2 ± 2.4

BUN

< 13.5>= 13.5

45.154.9

Creatinine

< 0.405>= 0.405

3.9096.1

SpO2

< 92.192.1 to 95.395.3 to 98.4>= 98.4

10.223.644.921.3

96.1 ± 3

BS_CONGESTION

YesNo

0.5399.5

BS_RHONCHI

YesNo

0.4399.6

BS_ABNORMAL

YesNo

3.8796.1

BS_DECREASED

YesNo

2.2997.7

BS_COURSE

YesNo

0.9099.1

BS_WHEEZES

YesNo

2.8497.2

BS_NO_COUGH

YesNo

0 + 100

BS_STRIDOR

YesNo

.08399.9

BS_CLEAR

YesNo

44.056.0

BS_CRACKLES

YesNo

0.7299.3

BS_RALES

YesNo

0.1199.9

BS_ABSENT

YesNo

.030 100

BS_INSPIRATION

YesNo

0.7999.2

BS_TUBULAR

YesNo

.024 100

BS_INFREQUENT

YesNo

0.6299.4

BS_STRONG

YesNo

0.7699.2

BS_FINE_CRACK...

YesNo

0.3199.7

BS_EXPIRATION

YesNo

0.9099.1

BS_NOT_CLEARING_SECREA...

YesNo

0.1099.9

BS_FREQUENT

YesNo

1.1998.8

BS_WEAK

YesNo

0.1699.8

BS_NON_PRODUCTIVE_CO...

YesNo

1.7498.3

BS_PRODUCTIVE_CO...

YesNo

1.8198.2

BS_MODERATE

YesNo

1.3698.6

BS_CLEARING_SECREA...

YesNo

0.4599.6

ChiefComplaint

RESPIRATORY COMPLAINTFEVERABD PAINORTHO INJURYCHEST PAINNEURO COMPLAINTFALLTRAFFIC INJURYABD PROBLEMSCHEST PRESSUREBACK PAINWEAKNESSSYNCOPEENT PROBLEMBODY ACHESCV COMPLAINTSHEADACHEDIZZYFLANK PAINCV PROBLEMSASSAULT RAPEPSYCHIATRICCHEST HEAVINESSSKIN COMPLAINTSPECIFIC DIAGNOSISDIABETICPAIN CHESTHEART RACETRAUMAGENITOURINARY PROBLEMPALPITATIONSHEART IRRALLERGIESHIGH BPFLUID NUTRITIONCONVULSIONSINFECTIONRAPID HRIRR HEARTBEATLACERATIONINGESTIONBP HIGHUNCONSCIOUSNESSVAGINAL BLEEDINGMED REFILLUNKNOWNLOW BPCARDIAC ARRESTEYE PROBLEMBP LOWother-

32.46.966.054.264.123.693.623.503.453.102.822.792.282.191.881.881.831.771.430.920.870.860.820.780.510.440.370.330.310.310.310.300.290.280.270.250.200.190.160.160.160.130.11.098.091.087.064.059.055.0540.18

NLP_FINDINGPositiveNegative

25.974.1

Agenda• Why?• Introduction: Bayesian Diagnostic Networks





The Process of Data-Based Research(finding the right data)

Identify Research Problem

Determine Subject Availability

Clinical Researcher

Clinical Researcher + Data Analyst + Terminologist

Query Database

Determine Data Availability

Clinical Researcher + Data Analyst + Terminologist

Query Database

Collect/Analyze Data

Clinical Researcher + Data Analyst + Terminologist+ Statistician

Query Database

Data Review/Analysis

Review Results Clinical Researcher

Data discovery and extraction takes 80-90% of the time.

Building a System to Automate Predictive Modeling

• Build a System That Can:• Identify the Target Patients• Identify Relevant Data Elements• Extract Patients and Data from the EDW/AHR• Provide Initial Analyses• Support Refinement

• The Key is Teaching the System a Certain Amount of Medical Knowledge• Ontologies: Tools For Capturing Complex

Medical Knowledge

Ontology-Driven Model Discovery• Can we use knowledge embedded in

ontologies to drive research?

• The Ontology would:

• Help select research patients

• Identify and extract relevant data

• Provide preliminary analysis of the data

• Allow visualization of this data

• Return Data and results to the user for further

study

• A tool to support Medical Data MiningAnalytic Health

Repository

DiseaseOntology

Concept Retrieval (from Ontology

Concept Translation to EDW Representation

Output

20%

20%

20%20%

20%

iii dfPdP

dfPdPfdP

)|()(

)|()()|(

Prediction Algorithm

Analysis ResultsAnalytic Data

Relevant Ontologic Concepts

Analysis Design Utility

Analytic Workbench· Screening Models· Model Comparisons· Model Explanation

(by reference to the Ontology)

Natural Language

Processing Subsystem

Structural Knowledge Retrieval from the Ontology

Data Retrieval from the Analytic Health Repository

Ontologies Describe How Diseases Are Related(according to ICD9)

Pneumococcal pneumoniaPneumococcal pneumonia

ICD9: 481

Other Bacterial PneumoniaOther bacterial pneumonia

ICD9: 482

Streptococal PneumoniaPneumonia due to Other

StreptococcusICD9: 482.3

BronchopneumoniaBronchopneumonia,

organism unspecifiedICD9: 485

Viral PneumoniaViral pneumonia

ICD9: 480

Staphlococcal PneumoniaPneumonia due to

StaphylococcusICD9: 482.4

Hemophilus PneumoniaPneumonia due to

Hemophilus influenzae ICD9: 482.2

Pseudomonas PneumoniaPneumonia due to

PseudomonaICD9: 482.1

Pneumonia

More Bactierial Pneumonias

Staph Aureus PneumoniaPneumonia due to

Staphylococcus, unspecifiedICD9: 482.40

MSSA Staph PneumoniaMethicillin Susceptable

Staph Aureus (MSSA) Pneumonia

ICD9: 482.41

MRSA Staph PneumoniaMethicillin Resistant Staph Aureus (MRSA) Pneumonia

ICD9: 482.42

Other Staph PneumoniaOther Staphylococcus

pneumoniaICD9: 482.49

Bacterial Pneumonia More Pneumonias

Ontologies Describe How Clinical Data are Related to Diseases

has_X-ray_Manifestation

PneumoniaPneumonia, Organism

unspecifiedICD9: 486

Pneumococcal pneumoniaPneumococcal pneumonia

ICD9: 481

Pneumonia

More Bacterial Pneumonias

Bacterial Pneumonia More Pneumonias

has_Sign

White Blood CountHematology: White

Blood CountLOINC: 62239-9

has_Altered_Lab_Value

Pulmonary RalesSigns: Chest

Auscultation-RalesPTXT:

28.1.3.22.34.2.1.32

TemperatureVital Signs:

TemperatureLOINC: 8310-5

has_Altered_VS

Localize InfitrateX-ray Finding:

Localized InfiltrateSNOMED: 128309002

has_Micro_Manifestation

Other Bacterial PneumoniaOther bacterial pneumonia

ICD9: 482

More Manifestations

has_??_Manifestation

Sputum Culture: Positive

SNOMED: 442773002

+ Sputum Culture

Visualizing the Results

Comparing Two Models Using the ROC Curves

Inspecting the Tradeoffs in Accuracy

Extensions of Diagnostic Modeling

• Large Models• Redundant Data• Equations and Logic

• Temporal Models• Following Disease Over Time• Summarized Data as Features

PNEUMONIA2

AbsentPresent

94.45.61

PNEUMONIA

AbsentPresent

95.34.71

Admit Dx: Pneumonia

PresentAbsent

4.7295.3

AGE

< 15.515.5 to 45.5>= 45.5

8.4142.349.3

42.8 ± 21

TEMP

< 36.7536.75 to 37.3537.35 to 38.05>= 38.05

75.620.53.440.49

36.63 ± 0.38

WBC

< 11.8511.85 to 15.15>= 15.15

81.211.77.07

11.1 ± 2.1

NLP_FINDING

NegativePositive

67.132.9

TEMP1

< 36.7536.75 to 37.3537.35 to 38.05>= 38.05

78.817.13.120.95

36.61 ± 0.39

WBC1

< 11.8511.85 to 15.15>= 15.15

100 0 0

10.2 ± 0.95

NLP_FINDING1

NegativePositive

65.934.1

TEMP2

< 36.7536.75 to 37.3537.35 to 38.05>= 38.05

77.018.24.120.67

36.62 ± 0.39

WBC2

< 11.8511.85 to 15.15>= 15.15

81.410.77.86

11.1 ± 2.2

NLP_FINDING2

NegativePositive

65.734.3

TEMP3

< 36.7536.75 to 37.3537.35 to 38.05>= 38.05

76.818.23.781.26

36.63 ± 0.41

WBC3

< 11.8511.85 to 15.15>= 15.15

83.410.85.75

10.9 ± 2

NLP_FINDING3

NegativePositive

66.833.2

CC

RESPIRATORY COMPLAINTABD PAINORTHO INJURYNEURO COMPLAINTFALLCHEST PRESSURECHEST PAINABD PROBLEMSWEAKNESSTRAFFIC INJURYother-

54.55.093.343.143.112.732.332.232.021.9219.6

PNEUMONIA1

PresentAbsent

5.0395.0

PNEUMONIA3

PresentAbsent

5.6194.4

PNEUMONIA4

PresentAbsent

6.9893.0

Simple Temporal Model

Time Slice 2Time Slice 1 Time Slice 3

A diagram of a simple clinical model(A Data Object)

data 9.6 x 103

quals

White Blood CountWBCLabObs

data Whole Blood

Specimen TypeSpecimenType

data Specimen Hemolyzed

CommentCommment

Clinical Element Model for White Blood Count

Units Cells per CC

What Does a Medical Concept Look Like(in probability space)

Concepts vary based on source, goals, and usage.

Pneumonia• Present• Absent

White Blood Count• Specimen Type• Units• Value

Pulmonary Infiltrate (Chest X-ray Report)• Present• Possible• Absent• Unknown

Cough• Present• Absent• Unknown

Simple Concept

Numeric Object

Human Reported Concept

Human Reported Concept(extended value set)

What Does a Concept Look LikeSome concepts have subconcepts.



Numeric Concept

Concept values

Subconcepts

Value• Real Number

Units• Mg per Deciliter• Grams• Cells per CC• …

Specimen Type• Blood• Pleural Fluid• Ascitic Fluid• …

Categorical Concept

What Does a Concept Look LikeConcepts can Modeled Probabilistically

Simple Concept

Numeric Concept

Human Reported Concept

Human Reported Concept(extended value set)

Pneumonia

PresentAbsent

1.5098.5

Cough

PresentAbsentUnknown

4.2653.242.6

Pulmonary Infiltrate (Chest X-Ray Report)

PresentPossibleAbsentUnknown

6.223.3819.271.2

CBC_White_Blood_Count

Unavailable0 to 10001000 to 20002000 to 30003000 to 40004000 to 50005000 to 60006000 to 70007000 to 80008000 to 90009000 to 1000010000 to 1100011000 to 1200012000 to 13000>= 13000

95.4.022.0750.200.420.680.870.870.680.420.20.075.022.005.001

-203 ± 1500

White_Blood_Count_Units

mg per deciliterkilogramsgramscells per ccetc

16.711.133.35.5633.3

White_Blood_Count_Value

0 to 10001000 to 20002000 to 30003000 to 40004000 to 50005000 to 60006000 to 70007000 to 80008000 to 90009000 to 1000010000 to 1100011000 to 1200012000 to 13000>= 13000

0.491.664.419.2015.019.219.215.09.204.411.660.490.11.023

6010 ± 2000

White_Blood_Count_Specimen

BloodPleural FluidAcitic FluidUrine

82.04.002.0012.0

What Does a Concept Look LikeConcepts are (in part) defined by their relationships.

Pneumonia• Present• Absent



White Blood Count• Elevated• Normal• Reduced• Unavailable

Pulmonary Infiltrate• Present• Absent

Causes Reported As

Value Thesholds: High-9,000 Low-2,000

Specimen: BloodUnits: Cells/CC

Pulmonary Infiltrate

PresentAbsent

5.4194.6

Pulmonary Infiltrate (Chest X-Ray Report)

PresentPossibleAbsentUnknown

6.223.3819.271.2

Pneumonia

PresentAbsent

1.5098.5

CBC_White_Blood_Count

Unavailable0 to 10001000 to 20002000 to 30003000 to 40004000 to 50005000 to 60006000 to 70007000 to 80008000 to 90009000 to 1000010000 to 1100011000 to 1200012000 to 13000>= 13000

95.4.022.0750.200.420.680.870.870.680.420.20.075.022.005.001

-203 ± 1500

White_Blood_Count_Units

mg per deciliterkilogramsgramscells per ccetc

16.711.133.35.5633.3

White_Blood_Count_Value

0 to 10001000 to 20002000 to 30003000 to 40004000 to 50005000 to 60006000 to 70007000 to 80008000 to 90009000 to 1000010000 to 1100011000 to 1200012000 to 13000>= 13000

0.491.664.419.2015.019.219.215.09.204.411.660.490.11.023

6010 ± 2000

White_Blood_Count_Specimen

BloodPleural FluidAcitic FluidUrine

82.04.002.0012.0

White_Blood_Count

ElevatedNormalReducedUnavailable

0.304.15.09895.4

What Does a Concept Look LikeAnd there are a number of ways to compute Concepts.

Causes Reported As

Value Thesholds: High-9,000 Low-2,000

Specimen: BloodUnits: Cells/CC

Conclusion

• Graphical Probabilistic Models can capture the Semantics of Medical Diagnosis.

• These models can be manufactured using data collected during the course of care.

• Probabilistic models can participate in clinical care.

• Medical terminologies, embedded in Ontologies can help to develop these models.

Questions???

Comments and Questions

Probability and Semantics

Disease Finding

Concept Word

Whole Part

Pneumonia Cough

Mammal Mouse

Hand Thumb

P(A) P(B|A)

The arrows provide link across which we can reason

One way to think of semantics: a set of relationships between concepts

# 60

A diagram of a simple clinical model

data 138 mmHg

quals

SystolicBPSystolicBPObs

data Right Arm

BodyLocationBodyLocation

data Sitting

PatientPositionPatientPosition

Clinical Element Model for Systolic Blood Pressure

# 61

What if there is no model?

Dry Weight:Site #1

kg

Weight:Site #2

DrykgWetIdeal

70

70

# 62

Too many ways to say the same thing

A single name/code and value• Dry Weight is 70 kg

Combination of two names/codes and values• Weight is 70 kg

• Weight type is dry

Terminology

• Probability

• P(D) – Probability of Disease

• Implies a Ratio or Rate

• Names: Prevalence, Prior Probability

• Location Specific

64

PopulationinNumber

DiseasewithNumber

Population from a Specific Setting

More Terminology

Conditional Probability

• Probability of a Finding in a patient with a Disease

• Probability of a Disease in a Patient with a Finding

• Probability of Disease in a patient with Finding 1, Finding 2, neg Finding 3, Finding 4, no Finding 5, etc.

65

Number With Disease and FindingNumber with Disease

Number With Disease and FindingNumber with Finding

Number With Disease and a Group of FindingsNumber with the Group of Findings

Names for the Numbers66

MI No MI

2% 98% 100%

Prevalence

Prior Probability

P(D)

Yet Another View67

MI No MI

Chest Pain 16% 84% 100%

No Chest Pain 0.6% 99% 100%

Positive Predictive Value: P(D|F)

Negative Predictive Value:P(no D|no F)

Dividing by the Row Marginals

From Data to Probabilities

68

DIAGNOSIS PROB

Pneumonia 92%

Asthma 14%

Chronic Bronchitis 12%

Acute Bronchitis 8%

Data Data

BayesianCalculation

Bayes Equation69

)(

)()|(

FP

DandFPFDP

Probability of DiseaseWhen the Finding is Present

Probability of BothThe Disease and Finding

Probability ofFinding

Bayes Equation70

)(

)()|(

FP

DandFPFDP

From probability theory:P (F and D) = P (D) * P (F|D)

Bayes Equation71

)(

)|()()|(

FP

DFPDPFDP

Posterior DiseaseProbability

SensitivityPrior DiseaseProbability

Probability of Finding


)(

)|()()|(

FP

DFPDPFDP




)(

)|()()|(

PainChestP

MIPainChestPMIPPainChestMIP





?

75.002.0)|(

PainChestMIP


Simple Bayes• Patient has One and Only

One Disease

Multi-Membership Bayes• Patient has Any Group of

Disease• Each Disease is Evaluated

Independently

Bayesian Networks• Patient has Any Group of

Disease• Diseases are Evaluated

According to Their Collective (Joint) Behavior

75

)()( ii

DandFPFP

Add All of the Probabilities Of Having Both the Finding and Disease

)|()()( ii

i DFPDPFP



One Disease



Independently




76

)|()()|()()( iiii DFPDPDFPDPFP

Two States Apply for Each Disease: With and Without the Disease



One Disease



Independently




DiseaseDisease

IntermediateConcept

IntermediateConcept

Finding 1Finding 1 Finding 2Finding 2

Finding 3Finding 3 Finding 4Finding 4

P(F) is Determined from the Joint Effect of Child Nodes

on Their Parents



?

75.002.0)|(

PainChestMIP



08.098.075.002.0

75.002.0)|(

PainChestMIP

What about more findings?79

• The joy of recursion!

)(

)|()()|(

1

11 FP

DFPDPFDP F1= Chest Pain

F2= ST Elevation

F3= CK Increased

…. etc.

Suppor

ts Acc

urate

and Co

mplete

Orderi

ng Pro

cess

82

Modeling Medical Phenomena

Examples of Some of the Things that can be Modeled

83Noise

The Effect of Noise on the Diagnosis of Pneumonia• Noisy Lab (continuous) Data• Noisy Physical Exam (categorical) Data

Types of Noise• Bias• Imprecision

PneumoniaPresentAbsent

2.0098.0

Measured White Blood Count

0 to 22 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 3030 to 5050 to 8080 to 130>= 130

2.948.5915.921.521.616.28.813.090.810.290.18.006 0 + 0 + 0 +

8.17 ± 3.5

Real White Blood Count

0 to 22 to 44 to 66 to 88 to 1010 to 1212 to 1414 to 1616 to 1818 to 2020 to 3030 to 5050 to 8080 to 130>= 130

0.132.0913.333.533.513.42.610.820.450.17.055 0 + 0 + 0 + 0 +

8.15 ± 2.4

Source of Result

Small SDBig SDBias HighBias Low

25.025.025.025.0

Auscultated Rales

PresentAbsent

18.681.4

Rales Really There!

PresentAbsent

6.7093.3

Reported By

Medical StudentResidentAttendingPulmonologistOver Sensitive Med Student

20.020.020.020.020.0

Normal and LogNormal Distributions

Different Types of Normal Noise/Bias

Noise/Bias modeled with Simple Discrete Distributions

84Boolean Logic

Probabilistic Logic• If A and B then C

• P(C) = P(A and B) = P(A) * P(B|A)• If A or B then C

• P(C) = P(A or B) = P(A) + P(B) – P(A and B)

• In a Bayesian Network, the resolution of Linked Rules Occurs Automatically

B

PresentAbsent

20.080.0

A

PresentAbsent

1.099.0

E

PresentAbsent

5.0095.0

C: If A and B then C

PresentAbsent

0.2099.8

D: If A or B then D

PresentAbsent

20.879.2

I: If B and F and H = High Then I

PresentAbsent

4.0096.0

F: If A or B or E then F

PresentAbsent

24.875.2

G: If (C and D) or (E and F) then G

PresentAbsent

5.1994.8

H

HighMediumLow

20.060.020.0

Five Interconnected Rules

Four Variables

85Temporal Phenomena

Several Approaches to Temporal Modeling have been Proposed

Markov and Hidden Markov Models are Most Common • Called Dynamic or Temporal Bayesian Networks• Can Model Complex Disease Behavior• Trained from Data Organized in “Time Slices”• Can be Extended to Include Decisions and Utilities

• (become “Partially Observable Markov Decision Processes”)

86The Dynamic Bayesian Network

Can Model Changing Medical Phenomena

• Changes in the State or Status of a Disease

• Findings Caused by the Disease in it’s Various States

• Can be Used for Diagnosis, Prediction and Explanation

Disease_Status2

AbsentMildModerateSevereDead

74.510.45.443.715.89

Disease_Status1


81.67.544.603.123.13

Disease_Status


90.04.003.002.001.00

Test

NormalMildly AbnormalSeverely AbnormalPatient Deceased

85.78.404.871.00

Test1


78.011.17.873.13

Test2


71.413.09.705.89

First Time Slice Second Time Slice

87

Pancreatitis Over Time

Pancreatitis

AcuteRecoveringDischargeable

63.08.2328.8

Pancreatitis


39.728.931.4

Pancreatitis


25.049.025.9

Amylase

30 to 8080 to 140140 to 200200 to 600600 to 8500

26.218.617.619.218.5

981 ± 2000

Amylase

30 to 8080 to 140140 to 200200 to 600600 to 8500

30.317.717.020.314.7

816 ± 1800

Amylase

30 to 8080 to 140140 to 200200 to 600600 to 8500

30.617.415.123.513.3

761 ± 1700

Lipase

0 to 300300 to 600600 to 12001200 to 30003000 to 1.28e5

22.814.724.711.326.5

17900 ± 34000

Lipase

0 to 300300 to 600600 to 12001200 to 30003000 to 1.28e5

31.515.924.19.9818.6

12700 ± 30000

Lipase

0 to 300300 to 600600 to 12001200 to 30003000 to 1.28e5

35.718.721.59.9914.2

9830 ± 26000

WBC

4 to 66 to 88 to 99 to 1212 to 17

14.628.616.018.322.5

9.28 ± 3.4

WBC

4 to 66 to 88 to 99 to 1212 to 17

17.830.516.515.719.5

8.9 ± 3.3

WBC

4 to 66 to 88 to 99 to 1212 to 17

18.730.916.915.418.2

8.78 ± 3.3

Abdomenal Pain

PresentAbsent

57.942.1

0.421 ± 0.49

Abdomenal Pain

PresentAbsent

53.546.5

0.465 ± 0.5

Abdomenal Pain

PresentAbsent

51.848.2

0.482 ± 0.5

Pain

PresentAbsent

60.839.2

0.392 ± 0.49

Pain

PresentAbsent

57.342.7

0.427 ± 0.49

Pain

PresentAbsent

56.743.3

0.433 ± 0.5

Glucose

60 to 9090 to 103103 to 115115 to 140140 to 410

20.516.619.522.620.8

139 ± 81

Glucose

60 to 9090 to 103103 to 115115 to 140140 to 410

24.316.622.819.916.5

130 ± 74

Glucose

60 to 9090 to 103103 to 115115 to 140140 to 410

27.617.624.617.313.0

122 ± 68

First Time Slice Second Time Slice

Bayesian Networks and Diagnosis

Re-Purposing Clinical Data

Strategic Goals

Minimum goal: Be able to share applications, reports, alerts, protocols, and decision support with ALL customers of our same vendor

Maximum goal: Be able to share applications, reports, alerts, protocols, and decision support with anyone in the WORLD

# 90

Why do we need detailed clinical models?

# 96

How are the models used in an EMR?Data entry screens, flow sheets, reports, ad hoc queries

• Basis for application access to clinical data

Computer-to-Computer Interfaces

• Creation of maps from departmental/external system models to the standard database model

Core data storage services

• Validation of data as it is stored in the database

Decision logic

• Basis for referencing data in decision support logic

Does NOT dictate physical storage strategy

Core Assumptions

‘The complexity of modern medicine exceeds the inherent limitations of the unaided human mind.’~ David M. Eddy, MD, Ph.D.

‘... man is not perfectible. There are limits to man’s capabilities as an information processor that assure the occurrence of random errors in his activities.’~ Clement J. McDonald, MD

Ontologies, Concepts, and

Probabilities

The way from medical concepts to diagnostic models

# 101

Relational database implications

How would you calculate the desired weight loss during the hospital stay?

Patient Identifier

Date and Time Observation Type Observation Value

Units

123456789 7/4/2005 Dry Weight 70 kg

123456789 7/19/2005 Current Weight 73 kg

Patient Identifier

Date and Time Observation Type

Weight type Observation Value

Units

123456789 7/4/2005 Weight Dry 70 kg

123456789 7/19/2005 Weight Current 73 kg

computable semantics and probabilistic graphical models where probabilistic systems and semantics...

Documents

healthcare data slide

patient slide

medical data forms slide

original data

care setting slide

bayesian networks

enterprise data warehouses

probabilistic systems