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Decision Support Systems. Rule based Systems. if A then B If pump failure then the pressure is low If pump failure then check oil level If power failure then pump failure Uncertainty If A (with certainty x) then B (with certainty f(x))

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Rule based systems l.jpg
Rule based Systems

if A then B

  • If pump failure then the pressure is low

  • If pump failure then check oil level

  • If power failure then pump failure

    Uncertainty

    If A (with certainty x) then B (with certainty f(x))

    If C (with certainty x) then B (with certainty g(x)

    If we now get the information that A holds with certainty a and C holds with certainty c, what is the certainty of B?


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Rule based Systems – cont.

  • Ifblood glucose is low before lunch, then take less insulin in the morning

  • Model of the doctor

  • Easy to build ?

  • Easy to maintain ?

  • Easy to understand for clinicians and patients ?

  • Problems with uncertainty and variability




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Bayesian Networks

  • If tonsillitis then P(temp>37.9) = 0.75

  • If whooping cough then P(temp>37.9) = 0.65One could be lead to read this as rules. They shouldn't be. So a different notation is used:

  • P(temp>37.9 | whooping cough) = 0.65

  • P(temp>37.9 | whooping cough, tonsillitis)


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Bayesian Networks - cont

Bayes' theorem:

P(A | B)P(B) = P(B | A)P(A)

  • uses a global perspective

  • calculates the new probabilities correctly

  • in rule based systems you try to model the experts way of reasoning (hence the name expert systems), while with Bayesian networks you try to model dependencies in the domain itself


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Example diabetes

  • Predictions of blood glucose levels based on mathematical models of the carbohydrate metabolism

  • Illustrate the effect of changing e.g. insulin

  • Model of the patient

  • Can handle uncertainty and variability ?

  • Problems with various factors, e.g. stress, fever, alcohol, exercise etc.


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Comparing Neural Networks and Bayesian Networks

The fundamental difference between the two types of networks is that a perceptrone in the hidden layers does not in itself have an interpretation in the domain of the system, whereas all the nodes of a Bayesian network represent concepts that are well defined with respect to the domain.


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