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In 62% of the patients, abnormalities of mitral valve motion were found to occur as a result of an imbalance between valve size and ventricle volume, possibly leading to a mitral valve prolapse The occurrence of MVP in these patients is related to two different factors First, it is known that MVP is associated with a higher risk of arrhythmia Second, there is some evidence for an association between MVP and anxiety disorders Cardiac failure can also occur as a complication during refeeding (Kahn et al, 1991), especially in cases of simultaneous hypophosphataemia (Schocken et al, 1989) Malnutrition, electrolyte imbalance, and ipecac abuse (Mitchell et al 1987) can result in a possibly irreversible secondary cardiomyopathy Male AN patients with an increased heart rate are particularly susceptible to the possibility of congestive heart failure (Siegel et al, 1995).

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Studies of patients suffering from malnutrition have shown a decrease in heart size and a rotation of the heart s axis into a vertical position during the course of weight loss These changes were mostly reversible in the refeeding period Refeeding Oedema Peripheral oedema occurs in a substantial proportion of patients undergoing refeeding (Winston, 2000) This may be due to a rapid weight gain of several kilograms and is often alarming for patients and doctors Why rebound oedema typically develops during the initial weeks of refeeding is unclear, but its effect on body weight should be considered (Bihun et al, 1993) It is very unlikely that this phenomenon is caused by hypoproteinaemia, because this sort of oedema is associated with normal serum albumin levels.

Pituitary adenoma (arrows).

135,235,790 134,455,076 134,206,308 130,804,094 133,420,176 136,088,579 138,810,351 141,586,558 144,418,289 147,306,655 150,252,788 153,257,844 156,323,001 159,449,461 162,638,450 165,891,219 169,209,043 172,593,224 176,045,089 179,565,990

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It has been suggested that salt and water depletion lead to the development of secondary hyperaldosteronism, which might predispose to the development of oedema when water and salt intake is increased during refeeding From a clinical point of view this kind of oedema has to be differentiated from cardiac oedema, caused by heart failure, as well as renal and hepatic failure..

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In this chapter we have presented some of the traditional and the soft computing based approaches for case adaptation. Traditionally, the adaptation knowledge is acquired through interviews with human experts and coded manually into a CBR system. Three types of traditional adaptation strategies are described: reinstantiation, substitution, and transformation. These methods are both labor intensive and time consuming. Maintenance of the knowledge base is also dif cult. For machine learning based methods, soft computing techniques, including those based on fuzzy decision trees, back-propagation neural networks, Bayesian models, support vector machines, and genetic algorithms, are presented. One of the advantages of these techniques is that adaptation knowledge can be determined automatically from the cases, so that this knowledge is more robust. Furthermore, maintenance of this knowledge base becomes easier by relearning or retraining with the new cases available. Although we have presented only the broad principles, the particular algorithms can be formulated depending on the problems at hand. REFERENCES

It is clear that this algorithm is equivalent to the construction of an optimum weight spanning tree if only one parent is selected per attribute provided, of course, a suitable topological order has been fixed. If more than one parent may be selected, polytrees or general directed acyclic graphs may be learned. From the above theorems and their proofs it is clear that if there is a perfect map of the given distribution that is a directed tree or a polytree, then the K2 algorithm will yield a perfect map, provided a topological order compatible with a perfect map is fixed and an evaluation measure satisfying the presuppositions of the above theorems is used. For more general graphs, however, the greedy character of the algorithm can lead to a selection of wrong parent attributes. An example illustrating this is shown in Figure 7.15. Independent of the topological order used, a wrong parent attribute is selected, namely either C as a parent for D or D as a parent for C, although these two attributes are conditionally independent given A and B. Note that from the distributions shown in Figure 7.15 it is immediately clear that this behavior is independent of the evaluation measure. In order to cope with this drawback one may consider adding a step in which parent attributes are removed, again in a greedy fashion. With this approach the failure of the K2 algorithm in the above example is amended. If, for instance, A, B, and C have been selected as parents for the attribute D, this second step would remove the attribute C and thus the correct graph would be found. However, it should be noted that, in general, incorrect selections are still possible, so the result may still be suboptimal.

M14 (s) = exp{ 1 2 [ 2 ( 2 2s)]1/2 } 1 2 M23 (s) = M24 (s) = M34 (s) =

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