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By
Amos Otieno Olwendo
Thesis Research (May 2014),
Masters of Science(MSc) in Medical Informatics
06/10/15 1
.1 1INTRODUCTION
 WHO lists COPD as the 4th
leading cause of death
worldwide
 90% of COPD mortalities experiences in mid...
.1 2Previous Studies?
 Previous studies have focused on using PFTs
 Identify COPD phenotypes using variables
 classify ...
.1 3What’s new?
 Our model attempts NOT to use PFT
 Phenotyping is related to disease classification
 classifies COPD p...
Phenotype?
06/10/15 Amos Otieno Olwendo 5
.1 5Phenotypes in action?
06/10/15 Amos Otieno Olwendo 6
.1 6Research Scope
1. Determine the essential variables and parameters
2. Design the probabilistic model used in this rese...
.2 1LITERATURE REVIEW
8
9
•
10
.2 4Spirometry & Barriers
 FEV1 decline predicts the future of the patient
 Equipment and training costs
 Low confidenc...
.2 5What is Modeling?
research technique that
connects empiricism to theory,
and
experiments to theory construction
and...
.2 6Why PGMs?
1. Ability to handle vagueness as a result of:
i. Biased or incomplete understanding of the event at
hand
ii...
2.7 Why BNs?
14
 Representation: a directed acyclic graph (DAG)
 Composed of random variables X1, …, Xn organized as
 Q...
Knowledge Engineering
 Knowledge acquisition
-elicitation,
-collection,
-analysis,
-modeling, and
-validation
 Knowledg...
.2 9Circuit design and Noisy-OR
06/10/15 Amos Otieno Olwendo 16
.3 1METHODOLOGY
 Non-interventional(Observational)
 retrospective study [Experimental study]
 Conducted at Loghman Haki...
.3 2Methodology
 This study was conducted from
 August 2013 to January 2014
 This unit receives approximately
 420 COP...
.3 3Methodology
 Sample Size: 100 COPD + 100 Asthma
 The environment composed of
 Dr. Agin,
 2 resident physicians (wo...
.3 4Initial BN Design
06/10/15 Amos Otieno Olwendo 20
.3 5Checklist
 The checklist had 10 questions
 Parameter measures were conducted through
 self report and/or
 Observat...
3.6 COPD & Asthma Diagnosis
 Patient History
 History of present illness
 Past medical history
 Family history of COPD...
3.7 COPD & Asthma Diagnosis
 Pulmonary Function Test(PFT)s
 e.g. spirometry /
 Bronchodilators (Nebulizer)
 X-ray – if...
.3 8Data Collection…design
06/10/15 Amos Otieno Olwendo 24
06/10/15 25Amos Otieno Olwendo
.3 10Neural Network setup
06/10/15 Amos Otieno Olwendo 26
.3 11Data Analysis
 Primary tool: the Bayesian network
 Model Validation: NN based on LM algorithm
 The dataset was div...
.3 12Data Analysis
 Developed a C++ application –
 through cases analysis,
 assigns a real number between negative and ...
.3 13Reliability Analysis: SPSS
06/10/15 Amos Otieno Olwendo 29
.3 14Reliability Analysis: SPSS
06/10/15 Amos Otieno Olwendo 30
.4 1RESULTS: BN
Bayesian Network Classification of 40 COPD Test Cases
COPD Phenotype Number of Cases
Asthma 1
Asthmatic CO...
.4 2RESULTS: BN
Bayesian Network Classification of 40 Asthma Test Cases
Classification Number of Cases
Asthma 34
Asthmatic...
.4 3RESULTS: NN
Levenberg-Marquardt Algorithm Classification of 40 COPD Test Cases
COPD Phenotype Number of Cases
Asthma 2...
.4 4RESULTS: NN
Levenberg-Marquardt Classification of 40 Asthma Test Cases
Classification Number of Cases
Asthma 34
Asthma...
4.5 RESULTS: Summary
35
Category Bayesian Network
Percentage (%)
Classification of the
Test Data Set
Levenberg-Marquardt
A...
.4 6RESULTS : C++ (MLE(
36
.4 7RESULTS: C++(group plot(
37
.4 8RESULTS: C++
38
.5 1DISCUSSION
1. COPD burden worldwide is underestimated (could
be worse than it is)
2. COPD under-diagnosis and/or misdi...
.5 2SUGGESTIONS
1. Increased COPD Awareness at the community level
1.1 Anti-smoking campaigns
1.2 Reduced exposure to 2nd
...
.5 3SUGGESTIONS
2. Population-based screening (“Target Case Finding”)
2.1 whenever an individual shows up to a health care...
.5 4SUGGESTIONS : Screening Criteria
42
End! Thank you
06/10/15 43Amos Otieno Olwendo
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v2 3rd (11-13 June 2015) KNH and UON Conference-Research as a Driver for Science & Technology Innovation for Heath

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v2 3rd (11-13 June 2015) KNH and UON Conference-Research as a Driver for Science & Technology Innovation for Heath

  1. 1. By Amos Otieno Olwendo Thesis Research (May 2014), Masters of Science(MSc) in Medical Informatics 06/10/15 1
  2. 2. .1 1INTRODUCTION  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
  3. 3. .1 2Previous 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 06/10/15 Amos Otieno Olwendo 3
  4. 4. .1 3What’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 06/10/15 Amos Otieno Olwendo 4
  5. 5. Phenotype? 06/10/15 Amos Otieno Olwendo 5
  6. 6. .1 5Phenotypes in action? 06/10/15 Amos Otieno Olwendo 6
  7. 7. .1 6Research Scope 1. Determine the essential variables and parameters 2. Design the probabilistic model used in this research 3. Determine whether a given patient case has COPD 4. Identify the consequent COPD Phenotype 4.1 Emphysema 4.2 Chronic bronchitis 4.3 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 Asthma 6. Severity ; NEXT as in Figure 2.1 7. Determine cause-effect relationships among variables 7
  8. 8. .2 1LITERATURE REVIEW 8
  9. 9. 9
  10. 10. • 10
  11. 11. .2 4Spirometry & 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 11
  12. 12. .2 5What 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 06/10/15 Amos Otieno Olwendo 12
  13. 13. .2 6Why PGMs? 1. Ability to handle vagueness as a result of: i. Biased or incomplete understanding of the event at hand ii. World of Noisy observations iii. Phenomena not represented iv. Randomness of events in real-life 1. Human reasoning -based on facts and assumptions 2. Probabilistically – degrees of belief are adjustable based on evidence 3. Intuitive with a compact data structure 13
  14. 14. 2.7 Why BNs? 14  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
  15. 15. Knowledge Engineering  Knowledge acquisition -elicitation, -collection, -analysis, -modeling, and -validation  Knowledge representation and  Reasoning Types of CPDs Noisy-Or CPD– common for medical diagnosis applications  Sigmoid CPD – best design approach (personal opinion) 15
  16. 16. .2 9Circuit design and Noisy-OR 06/10/15 Amos Otieno Olwendo 16
  17. 17. .3 1METHODOLOGY  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 ) 17
  18. 18. .3 2Methodology  This study was conducted from  August 2013 to January 2014  This unit receives approximately  420 COPD patients and  4200 Asthma patients monthly 18
  19. 19. .3 3Methodology  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 checklist 19
  20. 20. .3 4Initial BN Design 06/10/15 Amos Otieno Olwendo 20
  21. 21. .3 5Checklist  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 21
  22. 22. 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 22
  23. 23. 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.) 23
  24. 24. .3 8Data Collection…design 06/10/15 Amos Otieno Olwendo 24
  25. 25. 06/10/15 25Amos Otieno Olwendo
  26. 26. .3 10Neural Network setup 06/10/15 Amos Otieno Olwendo 26
  27. 27. .3 11Data 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 27
  28. 28. .3 12Data 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 28
  29. 29. .3 13Reliability Analysis: SPSS 06/10/15 Amos Otieno Olwendo 29
  30. 30. .3 14Reliability Analysis: SPSS 06/10/15 Amos Otieno Olwendo 30
  31. 31. .4 1RESULTS: BN Bayesian Network Classification of 40 COPD Test Cases COPD Phenotype Number of Cases Asthma 1 Asthmatic COPD 5 Chronic bronchitis 13 General COPD 21 06/10/15 Amos Otieno Olwendo 31
  32. 32. .4 2RESULTS: BN Bayesian Network Classification of 40 Asthma Test Cases Classification Number of Cases Asthma 34 Asthmatic COPD 6 32
  33. 33. .4 3RESULTS: NN Levenberg-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 33
  34. 34. .4 4RESULTS: NN Levenberg-Marquardt Classification of 40 Asthma Test Cases Classification Number of Cases Asthma 34 Asthmatic COPD 6 34
  35. 35. 4.5 RESULTS: Summary 35 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
  36. 36. .4 6RESULTS : C++ (MLE( 36
  37. 37. .4 7RESULTS: C++(group plot( 37
  38. 38. .4 8RESULTS: C++ 38
  39. 39. .5 1DISCUSSION 1. 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 that 3.2 affect COPD development and progression 3.3 Such behavior may include: 3.3.1 increasing number of female smokers and 3.3.2 increasing number of teenage smokers 3. Worst hit populations are in middle to low-income countries (inadequate healthcare services) 39
  40. 40. .5 2SUGGESTIONS 1. Increased COPD Awareness at the community level 1.1 Anti-smoking campaigns 1.2 Reduced exposure to 2nd hand cigarette smoke (creation of designated smoking areas) 1.3 Cooking using firewood/cow dung in less ventilated environments 1.4 Air obstruction symptoms 1.5 Legal measures- who can smoke and or buy cigarettes 40
  41. 41. .5 3SUGGESTIONS 2. Population-based screening (“Target Case Finding”) 2.1 whenever an individual shows up to a health care worker 2.2 Maybe useful in identifying those at risk 2. 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) 41
  42. 42. .5 4SUGGESTIONS : Screening Criteria 42
  43. 43. End! Thank you 06/10/15 43Amos Otieno Olwendo

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