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Identification of the Independency of Variables and Prediction
of Outcome of Tubercolusis (TBC) Treatment using Dynamic
Bayesian Networks:
Initial work of A case study a Public Hospital, Jakarta-Indonesia
Ito Wasito
Bogor, 29 October 2015
Trilateral Scientific Meeting on Big Data, Data Bases, and Dynamic Analysis
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Outline
2
1. Background
2. Goals
3. Related Works
4. Approach
5. Scope of Problems
6. Review on Bayesian Networks and Dynamics Bayesian Networks
7. Implementation
8. Experimental Results
9. Conclusions
10. Future Works
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Faculty of Computer Science, University of
Indonesia
Labs:
1. IT Governance
2. Computer Networks, Architecture, High
Performance Computing
3. Digital Library and Distance Learning
4. E-Government
5. Enterprise Computing
6. Formal Methods in Software Engineering
7. Information Retireval
8. Pattern Recognition
26/11/2015
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Faculty of Computer Science, University of
Indonesia
In preparation to develop a Big Data Centre (
Collaboration with Australia Universities).
26/11/2015
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Background
The Highest Countries in TBC epidemic:
1.India
2.China
3.South Africa
4. INDONESIA (WHO,2012)
6-9
months
Model TBC Patient
Supervision
5
Outcome of TBC treatment
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6
Background
Dynamic Bayesian
Networks
Bayesian
Networks
6
DAG (Direct Acyclic Graph)
Variables relationships :
Probabilities and Graph
Causal
Easy to interpret
Static Model
Time Series
Temporal Dependent
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Approach
7 Graph
Structure
on
Dynamic
Bayesian
Networks Identificatio
n of
Independen
cy
Variables
on TBC
Patient
Independen
cy and
Dependenc
y variables
with
Outcome
Treatment
Prediction
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Goals
8
1. To identify relationships between variables in
TBC patients's data
2. To Identify variables independency in TBC
diseases
3. To identify the independency and dependency
of variables in TBC disease against treatment's
outcome.
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Scope of Problems
9
- 12 Variables of TBC Patient's Data.
- Treatment of Patient's Data at Persahabatan
Public Hospital, Jakarta-Indonesia.
- Software tools: CaMML1.4.1 ( Monash
University, Australia) and Netica Java API
Package.
- Performance evaluation: prediction accuracy
and logarithmic loss
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Related Works
• Effective in symptoms of disease prediction
• accuracy 100%
DBN to predict Osteoarthritic Knee Pain
A model based Bayesian Network for prediction of IVF
Success Rate
DBN to predict sequences of
organ failures in patients admitted to ICU
10
(Watt,et all,2008)
(Kakhki,2013)
(Sandri,et all,2014)
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Evaluation of a Dynamic Bayesian Networks to predict Osteoarthritic Knee Pain
11
(Watt,et all,2008)
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ICU Treatment Analysis
To predict the order of organ failure on patient under 7 days treatment.
Score change on Sequential Organ Failure Assessment (SOFA) of patient at ICU
Use Genie software
12
(Sandri,et all,2014)
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Revisit: Bayesian Networks
Graph representation from probablity distribution
DAG (Directed Acyclic Graph)
13
(Larranaga,et all, 2013)
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14
D-separation
d-separation : To determine whether a node is
independent with the other nodes.
d-separation denoted as:
d-sepG (X;Y|Z) if there is no active path between
node X ∈ X dan node Y∈Y at graph G
Relationship between node :
Direct
Indirect
14
(Direct relationship)
(Koller dan Friedman,2009)
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Node Relationships : Indirect
15
(Koller dan Friedman,2009)
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Bayesian Networks in Health Applications
16
(Lucas,2004)
Diagnosis Reasoning
• To develop a diagnosis model on one specific disease.
• Prediction of the model of outcome of treatment.
• Exploitation of knowledge from treatment process.
Treatment Selection
• Decision system support to choose the optimal treatment of disease.
Discovering functional interactions
• Molecular mechanism i.e. gene interaction
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Dynamic Bayesian Networks
Two Tuples: Prior Model dan Transition Model
Formula DBN
17
(Van Gerven,2008)
(Van Gerven,2008)
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Dynamic Bayesian Networks Components
18
(Perez-Ariza,2012)
1. Sets of defined node
2. Intra-slice links
3. Temporal ( Inter-slice links)
4. Conditional Probability Table for the first intersection of time
5. Conditional Probability Table for second intersection of time
(from different parents).
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CaMML (Causal Discovery via MML)
19
(Korb dan Nicholson, 2010)
Structure Learning
• Metropolis Algorithm with Markov Chain Monte Carlo (MCMC) search algorithm
Score based
• Minimum Message Length (MML)
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Minimum Message Length (MML)
20
(Korb dan Nicholson, 2010)
where:
h: data
N: number of variables in h.
pi : prior probability i.
i: index in h.
j: index not in h.
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The Comparisons of DBN Structure Learning Software
21
Name
Structure Learning Parameter
Learning
DBN
Algorithms
GUI URL
CaMML Yes Yes CaMML Yes bayesian-intelligence.com
Bayes Net
Toolbox*
Partial1/ 2-Step1
Yes
Yes BIC1,2,BDe2 No code.google.com/p/bnt/
BNFinder Partial1 ? BIC,BDe, MIT No bioputer.mimuw.edu.pl/software/bnf/
Global
MIT*
Partial1 No MIT No code.google.com/p/globalmit/
Tetrad No/Manual3 Yes (PC,others) Yes www.phil.cmu.edu/tetrad/
GeNIe No/Manual3,4 Yes (PC,K2,other) Yes genie.sis.pitt.edu
Banjo Yes No BDe No cs.duke.edu/~amink/software/banjo/
* Requires Matlab. GlobalMIT: May be possible to use Octave (free) instead of Matlab
1 Supports DBN learning with interslice arcs only (i.e. no arcs within time slices)
2 With DBmcmc extension (bioss.ac.uk/~dirk/software/DBmcmc/) but binary/ternary data/attributes only
3 No official support for learning DBNs. Can adapt BN algorithms using tier priors etc.
4 DBN parameter learning, but no structure learning. Supports DBN inference, unrolling etc.
(Black, 2013)
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CaMML (Causal Discovery via MML)
22
Data transformation example from Static Bayesian Networks to Dynamic Bayesian Networks dapat as shown in Figure 2 (Black, 2013).
Figure 2. Data Transformation
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Data Preprocessing
24
Data Category defined by Expert
Raw Data
(TBC electronic Data)
Pre-processed data
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12 Variables
25
1. Age
2. Sex
3. Sputum test (D1,D2,D3)
4. Treatment categories
5. Weight (B1,B2,B3).
6. Tubercolusis types ( lung or extra lung).
7. Take in anti TBC medicine regularity ( yes/no)
8. Outcome of the Treatment
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Implementation
input Data Input and Parameter MML
CaMML (Causal Discovery via MML)
The outcome of DBN justified by expert
opinion
26
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Structure Evaluation of DBN Graph
27
• Use package Netica J API
Prediction accuracy (%)
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Experiments with different outcomes
recovered
completed
fail
died
incomplete cured
change treatment location
success
(recovered,complete)
unsuccess
(fail,died, incomplete
cured obat,change
treatment locatio)
6 categories of treatment outcomes 2 Categories of treatment outcomes
28
(WHO,2013)
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Experiment’s Results I
29
DBN Graph of Experiemental Results I 1
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Experiments results 2
30
DBN Graph of Experimental 2
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Experiment’s Results 3
31
BN Graph of Experiments 3
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Experimen’st Results 4
32
BN Graph of Experiments results 4
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Experiments Results E
33
BN Graph of Experiements results 5 (Structure Model by Experts)
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Experiments results 6
34
BN Graph of Experiements Results 6 (Structure Model by
Expert)
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Accuracy Comparisons
35
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Conclusions
36
Graph Structure of Dynamic Bayesian Networks
As Prediction Model of Patient’s
Treatment Outcome of Tuberculosis
Could identify independency of variables on
Tuberculosis patient’s
treatment data
The independencies of variables can be identified by d-separation
algorithm
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Future Works
37
By adding more variables, the model prediction of TBC treatment may have better performances.
Can be applied to the others epidemic data such as dengue fever, influenza type disease ( bird flu) etc.
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Q & A
Thank you 38