ByAmos Otieno Olwendo

Thesis Research (May 2014), Masters of Science(MSc) in Medical

Informatics04/15/23 1

1.1 INTRODUCTION WHO lists COPD as the 4th leading cause of death

worldwide 90% of COPD mortalities experiences in middle

and low-income countries

COPD diagnosis is prone to under-diagnosis and misdiagnosis (reported in UK, Australia, Canada

e.t.c)

Known risk factors:- Smoking of tobacco, anti-1-trypsin (A1At), and air pollution are the known major risk factors

1.2 Previous Studies? Previous studies have focused on using PFTs Identify COPD phenotypes using variables classify COPD cases based on severity (Stage 1

- 4) with Stage 1 being Mild and Stage 4 Very Severe (Figure 2.1)

Use methods ;data-driven phenotyping techniques such as:- Cluster analysis PCA Factor analysis Discriminant analysis to define COPD phenotypes

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1.3 What’s new? Our model attempts NOT to use PFT

Phenotyping is related to disease classification

classifies COPD phenotypes based on Morphology (appearance) Function Behavior

We use a Bayesian Network We achieved a classification of 98.75% on the

test data set

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1.4 What’s a COPD Phenotype?

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1.5 Phenotypes in action?

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1.6 Research Scope1. Determine the essential variables and parameters2. Design the probabilistic model used in this research3. Determine whether a given patient case has COPD4. Identify the consequent COPD Phenotype

4.1 Emphysema4.2 Chronic bronchitis4.3 General COPD (amalgamation of bronchitis and

emphysema)4.4 Asthmatic COPD (amalgamation of asthma and

any other phenotype(s))5. Ascertain whether the given patient has Asthma6. Severity ; NEXT as in Figure 2.17. Determine cause-effect relationships among

variables

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2.1 LITERATURE REVIEW

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2.4 Spirometry & Barriers FEV1 decline predicts the future of the

patient Equipment and training costs

Low confidence in the use and interpretation of the results

Perceived lack of utility Quality assurance issues Physical demand from the patient to use

the spirometer – esp. by the elderly and those experiencing respiratory challenges

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2.5 What is Modeling?research technique that

connects empiricism to theory, and

experiments to theory construction and validation

Here: BN used as the knowledge base laws of probability theory as the reasoning engine

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2.6 Why PGMs?1. Ability to handle vagueness as a result of:

i. Biased or incomplete understanding of the event at hand

ii. World of Noisy observationsiii. Phenomena not representediv. Randomness of events in real-life

2. Human reasoning -based on facts and assumptions

3. Probabilistically – degrees of belief are adjustable based on evidence

4. Intuitive with a compact data structure13

2.7 Why BNs?

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Representation: a directed acyclic graph (DAG) Composed of random variables X1, …, Xn

organized as Query, Non-query, and Evidence variables

Each variable/factor has a corresponding CPT Parent-child relationships of variables are

represented as CPDs [ P(X1, …, Xn) ] Inference: exact and approximate Learning: both parameters & structure, with

complete or incomplete data (through MLE) Employs the use of Chain rule for BN

Knowledge Engineering Knowledge acquisition

-elicitation, -collection, -analysis, -modeling, and -validation

Knowledge representation and Reasoning

Types of CPDsNoisy-Or CPD– common for medical diagnosis applications Sigmoid CPD – best design approach (personal opinion)

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2.9 Circuit design and Noisy-OR

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3.1 METHODOLOGY

Non-interventional(Observational) retrospective study [Experimental study]

Conducted at Loghman Hakim: Heart &Lung Division

Tehran- a city with high levels of air pollution (especially during winter )

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3.2 Methodology

This study was conducted from August 2013 to January 2014

This unit receives approximately 420 COPD patients and 4200 Asthma patients monthly

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3.3 Methodology Sample Size: 100 COPD + 100 Asthma

The environment composed of Dr. Agin, 2 resident physicians (worked with Dr. Agin), and 2 nurses (1 translator + Dr. Agin-Patient contact) Amos

conducted a structured interviews and data was recorded using

a structured checklist19

3.4 Initial BN Design

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3.5 Checklist The checklist had 10 questions Parameter measures were conducted through

self report and/or Observation

Checklist design patients had to commit to their parameter

choices by choosing a number between 0 and 10

Each interview result was cross -checked with the reference standards

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3.6 COPD & Asthma Diagnosis

Patient History History of present illness Past medical history Family history of COPD and Asthma social history of the patient (exposure to irritants)

Physical Exam Review of systems Visual examination (include palpation and percussion) listing to the lungs (stethoscope), physical activity

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3.7 COPD & Asthma Diagnosis

Pulmonary Function Test(PFT)s e.g. spirometry / Bronchodilators (Nebulizer)

X-ray – if necessary

Vital signs Examine O2 and CO2 in the blood (pulse oximeter ) Blood pressure (sphygmomanometer exam.)

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3.8 Data Collection…design

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3.10 Neural Network setup

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3.11 Data Analysis Primary tool: the Bayesian network Model Validation: NN based on LM algorithm The dataset was divided into

60% for training 40% test

To ensure an even distribution and representation, we grouped cases based on phenotypes (per

group: target and control) then assigned identifications to case then

Through simple random sampling, we determined what cases to be used for training and testing respectively

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3.12 Data Analysis

Developed a C++ application – through cases analysis, assigns a real number between negative and

positive infinity to each patient case (using MLE)

loaded these results to SQL Server and R Statistical software to obtain graphical

outputs

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3.13 Reliability Analysis: SPSS

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3.14 Reliability Analysis: SPSS

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4.1 RESULTS: BNBayesian Network Classification of 40 COPD Test Cases

COPD Phenotype Number of Cases

Asthma 1

Asthmatic COPD 5

Chronic bronchitis 13

General COPD 21

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4.2 RESULTS: BN

Bayesian Network Classification of 40 Asthma Test Cases

Classification Number of Cases

Asthma 34

Asthmatic COPD 6

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4.3 RESULTS: NNLevenberg-Marquardt Algorithm Classification of 40 COPD

Test Cases

COPD Phenotype Number of Cases

Asthma 2

Asthmatic COPD 2

Chronic bronchitis 25

General COPD 10

None 1

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4.4 RESULTS: NN

Levenberg-Marquardt Classification of 40 Asthma Test Cases

Classification Number of Cases

Asthma 34

Asthmatic COPD 6

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4.5 RESULTS: Summary

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Category Bayesian Network Percentage (%)

Classification of the Test Data Set

Levenberg-Marquardt Algorithm)

Percentage (%) Classification of

the Test Data Set

COPD 97.50 92.50

Asthma 100 100

Overall 98.75 96.25

4.6 RESULTS : C++ (MLE)

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4.7 RESULTS: C++(group plot)

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4.8 RESULTS: C++

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5.1 DISCUSSION1. COPD burden worldwide is underestimated

(could be worse than it is)2. COPD under-diagnosis and/or misdiagnosis should

not pose the challenges it currently does to clinicians

3. Increasing cases of COPD could be as 3.1 a result of the changes in some social behaviors

that3.2 affect COPD development and progression3.3 Such behavior may include:

3.3.1 increasing number of female smokers and 3.3.2 increasing number of teenage smokers

4. Worst hit populations are in middle to low-income countries (inadequate healthcare services) 39

5.2 SUGGESTIONS

1. Increased COPD Awareness at the community level

1.1 Anti-smoking campaigns1.2 Reduced exposure to 2nd hand cigarette smoke

(creation of designated smoking areas)1.3 Cooking using firewood/cow dung in less

ventilated environments1.4 Air obstruction symptoms1.5 Legal measures- who can smoke and or buy

cigarettes40

5.3 SUGGESTIONS2. Population-based screening (“Target Case

Finding”)2.1 whenever an individual shows up to a health

care worker2.2 Maybe useful in identifying those at risk

3. Need for screening devices since 3.1 certain localities lack specialist and/or 3.2 equipment (PFT devices, other test materials

like bronchodilators, X-ray machines, maybe computers and or internet)

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5.4 SUGGESTIONS : Screening Criteria

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End! Thank you

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