Titlebook: Computational Intelligence Processing in Medical Diagnosis; Manfred Schmitt,Horia-Nicolai Teodorescu,Lakhmi C. Book 2002 Springer-Verlag B

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Generalizations and Extensions,ent an integrated approach involving both intelligent data analysis and knowledge acquisition from experts that supports the development and validation of operational protocols. The aim is to lower development cost through the use of machine learning and at the same time ensure high quality standard

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Generalizations and Extensions,pic in medical informatics. Here we present a method for prognosis of temporal courses, which combines temporal abstractions with Case-Based Reasoning. The method was originally generated for multiparametric time course prognosis of the kidney function. Recently, we have started to apply the same id

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Renewal Processes and Random Walks,suitable bedside decision support are needed to cope with this flood of information. A basic task here is the fast and correct detection of important patterns of change such as level shifts and trends in the data. We present approaches for automated pattern detection in online-monitoring data. Sever

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Generalizations and Extensions,abilitation circumstances evinced by traumatic injury. The potential effectiveness of such systems is addressed from several points of view. First, the ability of the underlying connectionist models to identify complex, highly nonlinear, and sometimes even counterintuitive patterns in trauma data is

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Limit Theorems for Stopped Random Walks,ers in medicine. Over the past 4 decades, since its inception, research techniques in the given field have proliferated. Approaches adopted have included the use of rule based approaches such as Decision Trees, Fuzzy Logic and Expert Systems, to the use of Multivariate Statistical Analysis. The past