Harvard-MIT Division of Health Sciences and Technology HST.951J: Medical Decision Support, Fall 2005Instructors: Professor Lucila Ohno-Machado and Professor Staal Vinterbo

6.873/HST.951 Medical Decision SupportFall 2005

Biomedical Decision Support

Lucila Ohno-Machado Staal Vinterbo Pete Szolovits

Medical decisions

Maximize value: • Prolong life • Increase quality of life

• Minimize pain • Minimize cost • Match available

resources

HIS

SMART CENTRAL

SMART = Scalable Medical Alert Response Technology

Figures by MIT OCW.

Location

DSM

SDM 2

LSM -

- di

SpO2 ECG Location

Location ( )

Equipment

Cricket beacons

Caregiver PDA

Pattern

Recognition

- Data analysis

- SpO- ECG - Location

priorities rections

via RFID

Patient PDA Cricket listener Sensors

Defibrillator

Cricket listener

1 2 3

SMART Central

Decision Theory

• Game theory – Statistics – Operations research

• Maximize utility – In many domains, this

means maximize $$$

Example of a Decision Problem

• College athlete considering knee surgery

• Uncertainties: – success in recovering perfect mobility – infection in surgery (if so, needs another

surgery and may loose more mobility)– survive surgery

Knee SurgeryDeath

Surgery Death Surgery II

0.05

0.95 Survival

No Surgery

0.05

No infec.

Infection

0.95

0.6

0.4

Poor mobility

0.05

Survival

0.95

Poor mobility

Poor mobility

Full mobility

Wheelchair

Death

Death

Full mobility

0

0

3

10

6

6

Expected Value of Surgery

Surgery

No Surgery

0.05

Infection

0.4

0.05

Survival

Death

0.95

Poor mobility

No infec.

0.95

0.6

Poor mobility Poor mobility

Full mobility

Death

Death Surgery II 0.05

0.95 Survival Wheelchair

Death

7.7

Full mobility

0

0

3

10

6

6

Sensitivity Analysis

•

Surgery

No surgery

Effect of probabilities in the decision 10

Expected Values 6

0 P(Death)

Sensitivity Analysis

•

Surgery

No surgery

Effect of probabilities in the decision 10

Expected Values 6

0

P(Full Mobility)

Knee SurgeryDeath

Surgery Death Surgery II

0.05

0.95 Survival

No Surgery

0.05

No infec.

Infection

0.95

0.6

0.4

Poor mobility

0.05

Survival

0.95

Poor mobility

Poor mobility

Full mobility

Wheelchair

Death

Death

Full mobility

0

0

3

10

6

5

Data

Model Building

Pattern Discovery

SystemEvaluation

Experiment Hypothesis

Predictive Models

Data

Model Building

Pattern Discovery

System Evaluation

Experiment Hypothesis

Objectives• Build models from existing data

– Pattern recognition

• Apply model to new data to predict an unknown feature such as: – Diagnosis – Prognosis (outcome)

Figures removed due to copyright reasons.

G

GG

G

G

CC

CC

C T

TT

TT

T

A

A

A

A

AA

A

GenomeChromosomes

DNA

Proteins Proteins

Cell

Proteins act alone or in complexes to perform many cellular functions

Genes contain instructions for making proteins

Genes

Figure by MIT OCW.

What kind of data?

Nucleus

GeneDNA

DNA bases

mRNA

Chain of amino acids

Cell membrane

ProteinRibosome

DNA Genome

Clinical Data Phenome

Physiology

RNA Transcriptome

Physiome Protein Proteome

Transcription

Translation

Figure by MIT OCW.

Coronary Disease

45

ECG Interpretation

R-R interval

S-T elevation

QRS duration

QRS amplitude

AVF lead

SV tachycardia

Ventricular tachycardia

LV hypertrophy

RV hypertrophy

Myocardial infarction

P-R interval

Risk Score of Death from AngioplastyUnadjusted Overall Mortality Rate = 2.1%

0

500

1000

1500

2000

2500

3000

0%

10%

20%

30%

40%

50%

60%

Risk

Number of Cases

0.4% 1.4%

53.6%

12.4%

21.5%

2.2%

0 to 2 3 to 4 5 to 6 7 to 8 9 to 10 >10

Risk Score Category

Num

ber o

f Cas

es

Mortality

62%

26%

7.6% 2.9% 1.6% 1.3%

Predicting Individual Outcomein Coronary Intervention

Logistic Prognostic Risk Regression Model Score Model

Age > 74yrs B2/C Lesion Acute MI Class 3/4 CHF Left main PCI IIb/IIIa Use Stent Use Cardiogenic Shock Unstable Angina Tachycardic Chronic Renal Insuf.

Odds Ratio p-value

2.51 0.02 2.12 0.05 2.06 0.13 8.41 0.00 5.93 0.03 0.57 0.20 0.53 0.12 7.53 0.00 1.70 0.17 2.78 0.04 2.58 0.06

beta Risk coefficient

0.921 2 0.752 1 0.724 1 2.129 4 1.779 3 -0.554 -1 -0.626 -1 2.019 4 0.531 1 1.022 2 0.948 2

Value

Informed consent

"Informed consent and good clinical practice require a discussion of these risks and benefits, but there is very little data on the degree to which patients comprehend the specifics of this information,"

The researchers found that, of the patients who received angioplasty 42 percent could not identify any risks, and 41 percent could not identify any benefits. For the surgery patients, 45 percent could not identify any risks and 22 percent could not identify any benefits. Furthermore, when asked to quantify the risks of the procedure, 78 percent of the angioplasty and 57 percent of the surgery patients could not.

Alexander et al, 52th ACC meeting

Overview of this Course

Individualized predictionfor decision support inmedical/biologicalproblems

•Theory -- how it works•Practicality -- when to apply •Implementation -- how to apply

Pre-Requisites

6034 -- Intro to AI (Machine Learning) basic statistics, including linear regression

If needed, we will consider optional refresherrecitations:

• basic linear algebra (mostly notation)• basic statistical tests • set theory

Course Structure

• Homeworks, individual (30%) • Midterm (30%) • Final Project • Presentation and write-up – 5 pages plus references,

figures, tables on the web(40%)

• No final exam

Slides available online.Office hours by arrangement. Password protection for posting articles: Username and

password.

Intro to Decision Theory and Decision Analysis

– Optimal classification performance of a model

– Cost functions – Individualized decisions

• Confidence in predictions • Decision trees

Source: DOE

Simple Models

– Artificial Intelligence • Nearest neighbors • Association rules • Learning from experts

– Statistics • Linear regression • Linear discriminant analysis

Analysis of Failure Times

• Survival analysis • Cox model • Assumptions required for

models • Alternatives

asymmetry

border

detail

color

borderdetail

detail detail

Supervised Methods I• Logistic Regression

– interpretation of coefficients – limitations

• Classification Trees – splitting functions

– pruning – forests

asymmetry

border

detail

color

border detail

< 2

R

< 2

A

detail

Y

> 10

detail

<2

Y “benigh” “benigh” “malig” “malig”

“malig” “benign” “malig” “benign”

Supervised Methods II

• Neural networks – Regularization – Mixture of experts

• Support Vector

34

2

4

.6

.5

.8

.2

.1

.3 .7

.2

Σ

Σ

.4

.2

Σ

Weights Weights Machines

– VC dimension

– Soft margins

Supervised Methods III• Rule-based approaches

– Rough sets – Fuzzy sets

Figures removed due to copyright reasons.

Unsupervised Learning

Clustering – Agglomerative/divisive – Hierarchical/nonhierarchical

– K-means, k-medoids – Multidimensional scaling – Visualization

Dimensionality Reduction

• Pre-processing – Discretization algorithms – Filtering, cleaning

• Compression – Principal components analysis – Partial least squares

• Variable/Model Selection – Multivariate strategies – Interpretation

Stochastic Search

• Approximate solution strategies –Greedy –Annealing –Genetic algorithms

– Ant colony optimization

– Other evolutionary approaches

Evaluation

• How good is the prediction? –Calibration –Discrimination –Bias and variance

• Strategies for evaluation when number of cases is small –Cross-validation–Jackknife –Bootstrap

LR

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0.00 0.20 0.40 0.60 0.80 1.00

1 - Specificity

Sens

itivi

ty

Score aNN

Improving Performance

Combining Models/Ensembles

• Boosting • Bagging • Stacking

Bioinformatics• Phylogenetic trees • Haplotype tagging (SNP patterns)

Figures by MIT OCW.

ACTCCAA

CCTGCTT

ACGCTAG

ACTGCAT

GTTGCAA2

Sugested General Books

• Duda R, Hart P, Stork Duda, Richard O., Peter E. Hart, and David G. Stork.D. Pattern Classification. 2nd ed. New York, NY: Wiley, 2001.

Pattern Classification ISBN: 0471056693.

Wiley Interscience($103)

• Hastie T, Tibshirani Hastie, Trevor, Robert Tibshirani, and Jerome Friedman.

The Elements of R, Friedman J. The Elements of Statistical Learning: Data Mining,

Inference, and Prediction. New York, NY: Springer, 2001. ISBN: 0387952845.

Statistical LearningSpringer ($67)

Decision Analysis Module

• Chernoff and Moses

Elementary Decision Theory. Dover ($12)

• Hunink et al Decision Making in Health and Medicine: Integrating Evidence and Values. ($65)

Chernoff, Herman and Lincoln E. Moses.Elementary Decision Theory. New York, NY:Dover Publications, 1986, c1959. ISBN: 0486652181.��

Hunink, M.G. Myriam and et. al. Decision Making in Health and Medicine: Integrating Evidence and Values. Cambridge, UK: Cambridge University Press, 2001. ISBN: 0521770297.