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## Decision Support Systems

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**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?**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**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)**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**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.**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.