A Clinical Decision Support System For Alzheimer´s Disease and Other Related Mental Disorders

•

October, 2015

FLAVIO LUIZ SEIXAS, PHD.

SIADE PROJECT

Participating Institutions

• Center for Studies and Research on Aging (CEPE-Rio),Vital Brazil Institute, Rio de Janeiro.

• Center for Alzheimer's Disease and Related Disorder (CDA-IPUB-UFRJ),Institute of Psychiatry, Federal University of Rio de Janeiro.

• Institute of Computing, Federal Fluminense University (IC-UFF), Niterói.

• Midiacom Lab, Federal Fluminense University, Niterói.

• Medical Sciences College, Rio de Janeiro State University, Rio de Janeiro.

• National Laboratory for Scientific Computing (INCT), Brazil.

• Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro.

• King’s College London (KCL).

Agenda

• Motivation

• Objectives

• Clinical decision modeling

• Achievements

• Principal challenges and future works

Motivation

• Alzheimer’s disease represents 50-80% of dementia cases.

• Dementia has a prevalence of 7.8% of elderly from a local

community of São Paulo. Herrera et. al. (2002)

• Another survey indicated 6.9% of elderly from São Paulo.

Alzheimer’s represented 59% of dementia cases. Bottino et. al. (2006)

• Dementia has a prevalence from 4.6% to 9.7% of elderly. Rodriguez et. al. (2008).

• In 2020, Brazil will occupy the sixth worldwide ranking in

terms of elderly population.

Motivation

Decision support systems have been designed for helping

physician in clinical decision making.

Benefits:

• Ability to address the information overload that

physicians face.

• Integrating evidence-based knowledge.

Objective

Design and develop a clinical decision support system for

diagnosis of Dementia, Alzheimer`s Disease and Mild

Cognitive Impairment.

Why?

• World-wide population aging.

• High prevalence of Dementia among elderly.

• Early diagnosis of Alzheimer’s Disease can improve

the treatment efficiency, patient quality of life and

reduce the costs for public health systems.

CDSS - Principal Components

Physician

Mobile application

Communication interface

Inference engine

Knowledge base

Ask for a decision support for diagnosis.

Internet

HTTP messages

Provides suggestions for possible diagnosis that match a patient signs and symptoms.

Clinical decision support system

Published references related to diagnosis

criteria

Knowledge acquisition

Normal controls and patients’ clinical

records

Decision Modeling Process

Decision modeling for

a disease Identify the diagnosis

guideline for the disease

Diagnosis criteria for

the disease

Preprocess the clinical records of patients and normal controls

Training database

Build a Bayesian network structure

Perform Bayesian parameter learning

Evaluate the Bayesian learning

Deploy the decision model Acceptable

performance measures?

Review the decision model

Additional attributes

Additional clinical records

Decision model modeled

No

Yes

Patient carerequested

Take patient medical history and/or carry out clinical examinations for

dementia screening

Does the patient have

possible dementia?

Carry out neuropsychological tests for

Dementia

Carry out treatment for other diseases

Treatmentfollow-up (*)

If diagnosis of Dementia confirmed?

Carry out psychological tests exams for Mild

Cognitive Impairment

Carry out neuropsychological tests and exams for Dementia

due to Alzheimer s Disease


Treatment forDementia due to

Alzheimer s Diseasefollow-up (*)


Treatment forMild Cognitive

Impairmentfollow-up (*)

If diagnosis of Alzheimer s Disease

confirmed?

No Yes

Yes

No

If diagnosis of Mild Cognitive

Impairment confirmed?

No Yes No Yes

Dia

gnos

is o

f D

emen

tia,

Alz

heim

er s

Dis

ease

and

Mild

Cog

nitiv

e Im

pair

men

t

(*) A treatment should be defined by a physician

Diagnosis Process for Dementia, AD and MCI






the disease


Training database










No

Yes

Preprocess the patients’ health

records Integrate the patients’ health records spread

across multiple spreadsheets in one

training database

Database balancing

Attributes selection

Discretize numerical attributes

Training database

preprocessed

Preprocessing the Health Records

positive 135

negative 45

Alzheimer’s Disease

Dementia

Mild Cognitive Impairmentnegative

67

positive 180

negative 35

positive 32

Composed by:• Normal controls and patients’ health records provided by Center for Alzheimer's

Disease and Related Disorder, Institute of Psychiatry, UFRJ.Project approved by Research Ethics Committee (2012).

Training Database

positive 135

negative 45

Alzheimer’s Disease Dementia Mild Cognitive Impairment

negative 67

positive 180

negative 35

positive 32

Befo

re b

alan

cing

Afte

r bal

anci

ng

negative 35

positive 32

negative 134

positive 180positive

135

negative 90

Data Balancing

Method:SMOTE (Synthetic Minority Over-sampling Technique)1

1: Chawla, N. V.; Bowyer, K. W.; Hall, L. O.; Kegelmeyer, W. P. SMOTE: Synthetic Minority Over-Sampling Technique. Journal of Artificial Intelligence Research, v. 16, p. 321-357, 2002.

Attribute( MD( Entropy(

Mini$mental*state*examination*score* 5* 0.2791*

Clinical*Dementia*rating*scale* 11* 0.2441*

Pfeffer*questionnaire*score* 12* 0.2074*

Verbal*fluency*test*score* 8* 0.1665*

Clock*drawing*test*scale* 12* 0.0881*

Trial*making*test*scale* 40* 0.0829*

Age* 4* 0.0684*

Lawton*scale* 58* 0.0342*

IQCode*score* 56* 0.0324*

Stroop*color*word*test* 60* 0.0209*

Gender* 9* 0.0001*

Depression* 16* 0.0001*

Education*level* 2* 0.0423*

Rey*Complex*Figure* 78* 0.0181*

Cambridge*Cognitive*Examination* 79* 0.0000*

Digit*symbol* 81* 0.0000*

Neuropsychiatric*inventory* 56* 0.0000*

Cornell*depression*scale* 62* 0.0000*

Timed*Up*and*Go* 64* 0.0000*

POMA* 85* 0.0000*

Sit$to$Stand*test* 97* 0.0000*

Digit*span*test* 62* 0.0000*

Rey*Auditory$Verbal*Learning* 93* 0.0000*

Brain*anatomical*structures*volume* 83* 0.0000*

Criteria:Attributes filtered by missing

data rate (MD<60%)

AND

Information Gain

(Entropy>0.00001)

MD = Missing data ratio. It is calculated by

the ratio between the number of missing

data records and the total number of

records of the corresponding attribute.

Attributes Selection

Bayes’ Rule

Bayes’'rule:'

P(h | e) = P(e | h) ⋅P(h)P(e) !

the probability of a hypothesis h conditioned upon some evidence e is equal to its

likelihood P(e | h)!times its probability prior to any evidence P(h), normalized by

dividing P(e).

Definition: after applying Bayes’ theorem to obtain P(h | e) adopt that as your

posterior degree of belief in h, or Bel(h) = P(h | e).

Given dichotomous random variables (takes on one of only two possible values when

observed or measured):

P(h | e) = P(e | h) ⋅P(h)P(e | h) ⋅P(h)+P(e |¬h) ⋅P(¬h) !

Xi

Xj

Predictive

reasoning

Diagnostic

reasoning

Bayesian Network

Bayesian(network:(

Bayesian(network(is(a(graphical(structure(that(allows(us(to(represent(and(about(an( uncertain( domain.( The( nodes( in( a( Bayesian( network( represent( a( set( of(random(variables(X"="X1,"…"Xi,"…Xn.(A(set(of(directed(arcs((or(links)(connect(pairs(of(nodes(Xi"!"Xj,(representing(the(direct(dependencies(between(variables.(

Example:

Suppose that we have this very simple model of flu causing a high temperature with

the following prior and conditional probabilities distribution values.

If an individual has a high temperature (i.e., the evidence available is Hi=True), the

computation for this diagnostic reasoning is as follows:

Bel(Flu = True) =α ⋅P(Hi = True | Flu = True) ⋅P(Flu = True) =α ⋅0.05 ⋅0.9 =α ⋅0.045

Bel(Flu = False) =α ⋅P(Hi = True | Flu = False) ⋅P(Flu = False) =α ⋅0.95 ⋅0.2 =α ⋅0.19!

Pr(Flu=True) 5%

Pr(Flu=False) 95%

Pr(Hi=True | Flu=True) 90%

Pr(Hi=False | Flu=True) 10%

Pr(Hi=True | Flu=False) 20%

Pr(Hi=False | Flu=False) 80%

Bayesian Network

If an individual has a high temperature (i.e., the evidence available is Hi=True), the

computation for this diagnostic reasoning is as follows:

Bel(Flu = T ) =α ⋅P(Hi = T | Flu = T ) ⋅P(Flu = T ) =α ⋅0.05 ⋅0.9 =α ⋅0.045

Bel(Flu = F) =α ⋅P(Hi = T | Flu = F) ⋅P(Flu = F) =α ⋅0.95 ⋅0.2 =α ⋅0.19

Bel(Flu = T )+Bel(Flu = F) =1 given that variable states are mutually exclusive.

So,α ⋅0.045+α ⋅0.19 =1∴α = 10.045+ 0.19

Bel(Flu = True) = 0.0450.19+ 0.045

= 0.19

Bel(Flu = False) = 0.190.19+ 0.045

= 0.81

Bayesian Network






the disease


Training database










No

Yes

…"Background information

(predisposal factors)

…"

Query node

(disease)

Findings (symptoms, signs,

neuropsychological tests results)

U

D B1 B2 Bn

Q

F1 F2 Fm

Utility function

Decision box

Generic Bayesian Network Structure






the disease


Training database










No

Yes

Bayesian Learning

Objective:!learn!the!most!probable!h!given!data!D#=#{#Xi#;#di#}#!For-each-h-∈ -H:-

Calculate!P(h |D)∝P(D | h) ⋅P(h) !!Bayesian-estimators:-

Maximum!A!posteriori!Probability:!!hMAP = argmaxP(h |D) = argmaxP(D | h) ⋅P(h) !

!Maximum!Likelihood:!

hML = argmaxP(D | h) !

Discretize Numerical Attributes

Minimum&Description&Length&(MDL)&(1):&Occam’s razor: choose the shortest explanation for the observed data.

hMAP = argmaxP(D | h) ⋅P(h)hMAP = argmax lgP(D | h)+ lgP(h)[ ]hMAP = argmin − lgP(D | h)− lgP(h)[ ]

This equation can be interpreted as a statement that short hypotheses are preferred.

Assuming that LC(i) ≅ description length of message i with respect to C.

LCD|H (D | h) = − logP(D | h) , where CD|h is the optimal code for describing data D.

LCH (h) = − logP(h) , where CH is the optimal code for hypothesis space H.

So:

hMAP ∝ argminH∈h

LCD|h (D | h)+ LCH (h)#$ %&

1: Kononenko, I. On biases in estimating multi-valued attributes. International Joint Conference on Artificial Intelligence, 1995.

Lawrence Erlbaum Associates. p.1034-1040.

Bayesian Learning: EM Algorithm

Expectation-Maximization algorithm (1) (1/3):

• Find a maximum likelihood estimates for θ when given dataset is incomplete.

• Starts with random probability distributions.

• Alternates between two steps.

• Expectation step: “complete” the data set by using the current parameter

estimates θ̂ (calculate expectations for missing values).

• Maximization step: use the “complete” data set to find a new maximum

likelihood estimate θ̂ ' for the parameters.

1: Dempster, A. P.; Laird, N. M.; Rubin, D. B. Maximum likelihood from incomplete data via the EM algorithm. Journal of the

Royal Statistical Society. Series B (Methodological), v. 39, n. 1, p. 1-38, 1977. ISSN 0035-9246.


Expectation-Maximization algorithm (2/3):

Let:

yi – observable variables.

zi – latent variables.

θ – all possible parameters in the model.

Goal is to find:

θ̂ = argmaxθ

P(θ |D)

P(θ | yi,..., yn )∝P(y1...yn |θ ) ⋅P(θ )∝P(y1...yn |θ )

As P(y1...yn |θ ) = P(y1...yn, z1...zn |θ )∫ dz


Expectation-Maximization algorithm (3/3):

Using the auxiliary function:

Q(θ |θt ) = P(z1...zn |θt, y1...yn )∫ logP(θ, z | y1...yn )dz

What EM algorithm does is:

θt+1 = argmaxQ(θ |θt ) , with random starting point.

E-Step: find the probabilities for z1…zn if all parameters are fixed to θt

M-Step: now that P(z1...zn |θt, y1...yn ) is fixed, find θ that maximizes the integral.

Utility

Dementia? 6%

94%

>13

0-13

Education

82%

18%

Female

Male

Gender

56%

44%

>72

0-72

Age

58%

42%

Positive

Negative

Diagnosis

1%

1%

16%

21%

50%

12%

5

4

3

2

1

0

Clock Drawing Test (CDT) scale

20%

41%

39%

27-30

18-26

0-17

Mini Mental State Exam (MMSE) score

51%

46%

16%

>11

5-11

0-4

Verbal Fluency Test (VFT) score

19%

15%

32%

29%

6%

3-severe

2-moderate

1-mild

0.5-very mild

0-normal control

Clinical Dementia Rating (CDR) scale

72%

28%

>3.55

0-3.55

IQCode (Informant Questionnaire on Cognitive Decline in the Elderly) score

74%

26%

>9

0-9

Lawton scale

71%

29%

>15

0-15

Stroop color word test

72% 18% 10%

>59 17-59

0-16 Trial Making Test (TMT)

39% 61%

>51 0-51

Berg balance scale

78%

8%

14%

>2

1-2

0

Pfeffer questionnaire

32% 68%

Presence Absence

Depression

Utility

Alzheimer’s Disease? Dementia?

4%

96%

>13

0-13

Education

77%

23%

Female

Male

Gender

66%

34%

>72

0-72

Age

59%

41%

Positive

Negative

Diagnosis

0%

1%

11%

12%

60%

17%

5

4

3

2

1

0


4%

32%

64%

27-30

18-26

0-17


14%

60%

26%

>11

5-11

0-4


23%

22%

54%

1%

0%

3-severe

2-moderate

1-mild

0.5-very mild

0-normal control


71% 9% 20%

>59 17-59

0-16 Trial Making Test (TMT)

73%

27%

>15

0-15


77%

23%

>9

0-9

Lawton scale

81%

19%

>3.55

0-3.55


33% 67%

>51 0-51

Berg balance scale

97%

3%

0%

>2

1-2

0


37% 63%

Presence Absence

Depression

Positive

Utility

Mild Cognitive Disorder?

Dementia? 14%

86%

>15

0-15

Education

77%

23%

Female

Male Gender

48%

52%

>69

0-69

Age

59%

41%

Positive

Negative

Diagnosis

0%

0%

44%

28%

28%

0%

5

4

3

2

1

0


39%

61%

29-30

0-28


37%

63%

>15

0-15


2%

2%

32%

23%

41%

3-severe

2-moderate

1-mild

0.5-very mild

0-normal control


78%

22%

>36

0-36

Trial Making Test (TMT)

47%

53%

>17

0-17


64%

36%

>14

0-14

Lawton scale

69%

31%

>3.32

0-0.32


41%

22%

36%

>55

55

0-54

Berg balance scale

43%

57%

>1

0-1


45% 55%

Presence Absence

Depression

Negative Mild Cognitive

Impairment?






the disease


Training database










No

Yes

Bayesian Learning: Results Evaluation

1. Using cross-validation with 4 folds, we compared

Bayesian Network performance with other well-known

classifiers:• Näive Bayes

• Logistic Regression

• Multilayer Perceptron

• Decision Table

• Decision Stump using Boost algorithm

• J48 Decision Tree

2. Qualitative evaluation of sensitivity analysis results.


Classification performance measures:

Performance measure Acronym Domain Best score

Area under ROC curve AUC [0, 1] 1

Harmonic mean of

precision and recallF1 [0, 1] 1

Mean square error MSE [0, 1] 0

Mean cross-entropy MXE [0, ∞) 0





Bayesian Learning: Sensitivity Analysis

Next Challenges

1. Design and develop a prototype application.

http://siade.midiacom.uff.br

Future Works

2. Evaluate the decision support system in a real clinical

daily routine.

3. Improve the decision model with a continuous Bayesian

network learning process.

4. Extend the clinical decision model to other domains.

Future Works

About Bayesian modeling:

1. How to establish a continuous parameters adjustment method for Bayesian

models?

2. A higher missing data ratio may cause bias, imprecision or confounding. Is it

possible finding out a model for missing data? What should be a reasonable

level of missing data ratio?

3. The independence between random variables with same parent is an

assumption from Bayesian-based models. What is the better way to deal

with it? What are its effects in the Bayesian results?

Questions

About Dementia and other related mental disorders:

4. How could we define a health cost-effective analysis for utility node?

5. Is there any other patients database with normal controls that could be used

as training database for Bayesian learning?

6. How could we integrate the identified decision points of the current clinical

guidelines with the decision boxes of Bayesian networks?

Questions

About Decision-Support System:

7. Is there any health information system that we could integrate with our

decision-support model?

8. Depends on (7), how could we assure the semantic interoperability between

the knowledge base mapped on decision-support model and the health

information system?

9. Our decision-support system has focused on clinical diagnosis process. Is

there another health care area that is relevant for designing and developing

a similar decision-support system? (e.g., patient-centered treatment

planning, health monitoring system...)

Questions

This research was partially supported by:

• FAPERJ (Research Support Foundation of the State of Rio de Janeiro).

• CNPQ (National Council for Scientific and Technological Development).

Acknowledgements

Acknowledgements

I would like to thank…

Robin Morris, Daniel Stahl (King’s College London),

Jerson Laks (Federal University of Rio de Janeiro), and

Daniel Mograbi (Pontifical Catholic University of Rio de Janeiro)

for such opportunity.

And I thank you for the

audience!

…any question?

Acknowledgements

[email protected]

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A Clinical Decision Support System For Alzheimer´s Disease and Other Related Mental Disorders

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