overall presentation matram project
DESCRIPTION
TRANSCRIPT
MATRAM projectModelisation and Evaluation
Bayesian Belief Network applied to Public Health Programmes
What tool for appraising the linkages between inputs/outputs and impact indicators ?
Overall picture
BBN: Probabilistic models based on directed graphs
-knowledge representation and comprehensive data analysis framework-visual structure attractive for exploring and explaining complex problems
Bayesia Lab : Powerful software for deep understanding of very complex and high-dimensional problem domains such as Public Health Programmes…
Sequence of the presentation
1- Three hats game
2- S&P Market shares
3- Perception of students on Family Medicine
4- Dataset from retired lung specialist
5- Malaria program
6- Holistic HIV program description
7- Summary
Common vocabulary
• Nodes represent the variables of the domain (e.g. the temperature of a device, a feature of an object, the occurrence of an event, the age of a patient)
• Links represent statistical (informational) or dependencies among the variables
The dependencies are quantified by conditional probabilitiesfor each node given its parents in the network.
1- Three hats game
Monthy Hall problem
1- Three hats game
1- Three hats game
1- Three hats game
P(Money Blue I Yellow hat empty) = P(Yellow hat to be selected I Money Blue ) X P (Money Blue ) / P(Yellow hat selected or empty)
P(Yellow hat to be selected I Money Blue ) = 1/2
P (Money Blue ) = 1/3 still
P(Yellow hat selected)
= 1/3 X ½
= 1/3 X 1 1/3 x ½ + 1/3 x 1 + 1/3 x 0 = 1/6 + 1/3 = 1/2 = 1/3 X 0
1
1
2a
2b
2c
2a
2b
2c
P(Money Blue I Yellow hat empty) =
(1/2 x 1/3) / 1/2 = 1/3
Because money must be somewhere P = 2/3 for RED !
1- Three hats game
1- Three hats game
-Conditional Probality distribution (child node) GIVEN THESE two nodes
Ability to update your knowledge given that you obtain new information …
2- S&P Market shares
Unsupervised learning : The goal of structure learning is to learn a model that best explains the data
Model built up by expertise: Complex and high-dimensional problem domain such as Public Health Programme
2- S&P Market shares
The wealth of data in the financial markets is a fertile ground for experimenting with knowledge discovery algorithms and for generating knowledge representations in the form of Bayesian networks
Database available: Daily closing prices of all stocks included in the S&P 500 index from
January 3, 2005 through December 30, 2010
2- S&P Market shares
Discritization by Kmeans
Maximum Spanning Tree
1st BayesiaLab User Conference
16/10/12
Strategic partnership : Procter & Gamble
16/10/12
3- Perception of students on Family Medicine
Database available: Database related to the questionnaires for the Perception
of Family Medicine study among Tajik students.
3- Perception of students on Family Medicine
2614 students interviewed
3- Perception of students on Family Medicine
Unsupervised Data Clustering
Creation of a model to represent uniform groups of students/observations
3- Perception of students on Family Medicine
All variables are discrete
Clustering process
3- Perception of students on Family Medicine
4- Dataset from retired lung specialist
Reasoning under uncertainty applies in two ways:• Diagnosis (inference from effect to cause)• Simulation (inference from cause to effect)
smoking has adirect influence onbronchitis
Model already set by expertise… right connectivity between pre-existing nodes
4- Dataset from retired lung specialist
5- Malaria program
Bayesian networks they can be
machine learned from data,
or,
can be built from human knowledge, i.e. from theory.
Cancer Care Ontario
16/10/12
Exploring New Methods of Identifying the Most Complex Patients for TargetedInterventions
Earlier identification of these patients could enable clinicians as well as policy makers to target clinical
or policy interventions aimed at reducing costlyhealth system use
Database: Clinical and past health system use data readily
available on the first day of dialysis
5- Malaria program
Practical example Source: Annuaire statistique Burkina Faso 2012
Impact indicator
5- Malaria program
5- Malaria program
Districts
Demographic/contextual parametersTotal Population
InfrastructureHealth CentersPrivate nursing based centers Private Medicines stores
Human ressourcesNurses with diploma% of norm compliance with expected HR for Health center
Malaria programmeInputTotal cases Palu Simp Cases Palu Comp Moins5 Cases Palu Comp Plus5OutputNbre TDR réalisés chez les <5ans Nbre de TDR réalisés chez les plus de 5ansNbre de GE réalisées chez <5ans Nbre de GE réalisées chez les plus de 5ansOutcomeNbre de TDR positifs chez les <5ans Nbre de GE positives chez <5ans Nbre deTDR positifs chez les plus de 5ans Nbre de GE positives chez les plus de 5ansNbre de cas traités avec ACT moins de 5ans dans les FSNbre de cas traités avec ACT plus de 5ans dans les FSImpactNbre cas Décès Palu Comp Moins 5 Nbre cas Décès Palu Comp Décès Plus 5
5- Malaria program
5- Malaria program
The assumed impact of ITNs on mortality is based on a systematic review of five randomly controlled
trials of ITNs in which child mortality was measured as an outcome.
Global Fund’s approach
to estimating lives saved…
The review concluded that ITNs contributed to a 17% reduction in “all-cause” mortality among children under
five years compared to a scenario where no nets were used at all.
Possibility to set up a holistic malaria program description…model
6- Holistic HIV program description
Global Fund KPI performance framework
The modelling exercise works by comparing the estimated effects of current service delivery scenarios on mortality
Against
a hypothetical “no treatment, no intervention” scenario
6- Holistic HIV program description
Until early 2011, the model assumed a standard survival rate of 86% at 12 months and 90% for each subsequent year for both adults and children on treatment.
Under the no-treatment scenario, it assumed a survival rate of 50% at 12 months and 0% at 24 months from the point of needing to start treatment.
6- Holistic HIV program description
The more context-specific approach will reveal the substantial variation in the effectiveness of HIV program.
This variation arises from probability distribution in :-access and uptake rates, -quality of care, -availability of skilled health workers, -rates of compliance with treatment, -lost to follow-up ,-population coverage of other determinants of clinical effectiveness, such as access to good nutrition
Variations, attribution (and causality): To what extend do changes in the inputs and processes of program & health
system result in changes in health outcomes ?
7- Summery
16/10/12
1- Mathematical model can be applied to Public Health programmes
Constraints:-Diversity and quality of the data-Representativeness of the Model (to be developped with Swiss TPH label)
2- Functionalities of BayesiaLAb do meet partner’s expectations in terms of impact evaluation
Constraints:-Mastery over the use of software-Tailored communication (against specific technical clients’ expectations)
Thank you for
your attention
What algorithms does BayesiaLab use for learning network parameters?
16/10/12
Mathematical background
What types of algorithms does BayesiaLab use for learning network structures?
BayesiaLab has developed a score-based learning algorithms. As opposed to the constraint-based algorithms that use independence tests to add or remove arcs between nodes, we utilize the MDL score (Minimum Description Length) to measure the quality of candidate networks with respect to the available data.This score, which is derived from Information Theory, allows to automatically take into account the data likelihood with respect to the network and the structural complexity of the network.
Mathematical background
All the optimization criteria of BayesiaLab’s learning algorithms are based on information theory (e.g. the Minimum Description Length). With that, no assumptions of linearity are made at any point.
16/10/12
Mathematical background
16/10/12
Mathematical background