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# decision support systems - PowerPoint PPT Presentation

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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## PowerPoint Slideshow about 'decision support systems' - Mercy

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Presentation Transcript

### Decision Support 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?

• 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

• 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)

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

• 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.

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.