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Since the description of sepsis by Schottmüller in 1914, the amount on knowledge available on sepsis and its underlying pathophysiology has substantially increased. Epidemiologic examinations of abdominal septic shock patients show the potential for high risk posed by and the extensive therapy situation in the intensive care unit (ICU) (5). Unfortunately, until now it has not been possible to significantly reduce the mortality rate of septic shock, which is as high as 50-60% worldwide, although PROWESS' results (1) are encouraging. This paper summarizes the main results of the MEDAN project and their medical impacts. Several aspects are already published, see the references. The heterogeneity of patient groups and the variations in therapy strategies is seen as one of the main problems for sepsis trials. In the MEDAN multi-center study of 71 intensive care units in Germany, a group of 382 patients made up exclusively of abdominal septic shock patients who met the consensus criteria for septic shock (3) was analysed. For use within scores or stand-alone experiments variables are often studied as isolated variables, not as a multidimensional whole, e.g. a recent study takes a look at the role thrombocytes play (15). To avoid this limitation, our study compares several established scores (SOFA, APACHE II, SAPS II, MODS) by a multi-dimensional neuronal network analysis. For outcome prediction the data of 382 patients was analysed by using most of the commonly documented vital parameters and doses of medicine (metric variables). Data was collected in German hospitals from 1998 to 2001. The 382 handwritten patient records were transferred to an electronic database giving the amount of 2.5 million data entries. The metric data contained in the database is composed of daily measurements and doses of medicine. We used range and plausibility checks to allow no faulty data in the electronic database. 187 of the 382 patients are deceased (49 %).
Attraction and commercial success of web sites depend heavily on the additional values visitors may find. Here, individual, automatically obtained and maintained user profiles are the key for user satisfaction. This contribution shows for the example of a cooking information site how user profiles might be obtained using category information provided by cooking recipes. It is shown that metrical distance functions and standard clustering procedures lead to erroneous results. Instead, we propose a new mutual information based clustering approach and outline its implications for the example of user profiling.
Data driven automatic model selection and parameter adaptation – a case study for septic shock
(2004)
In bioinformatics, biochemical pathways can be modeled by many differential equations. It is still an open problem how to fit the huge amount of parameters of the equations to the available data. Here, the approach of systematically learning the parameters is necessary. This paper propose as model selection criterion the least complex description of the observed data by the model, the minimum description length. For the small, but important example of inflammation modeling the performance of the approach is evaluated.
In bioinformatics, biochemical signal pathways can be modeled by many differential equations. It is still an open problem how to fit the huge amount of parameters of the equations to the available data. Here, the approach of systematically obtaining the most appropriate model and learning its parameters is extremely interesting. One of the most often used approaches for model selection is to choose the least complex model which “fits the needs”. For noisy measurements, the model which has the smallest mean squared error of the observed data results in a model which fits too accurately to the data – it is overfitting. Such a model will perform good on the training data, but worse on unknown data. This paper propose as model selection criterion the least complex description of the observed data by the model, the minimum description length. For the small, but important example of inflammation modeling the performance of the approach is evaluated. Keywords: biochemical pathways, differential equations, septic shock, parameter estimation, overfitting, minimum description length.
In bioinformatics, biochemical pathways can be modeled by many differential equations. It is still an open problem how to fit the huge amount of parameters of the equations to the available data. Here, the approach of systematically learning the parameters is necessary. In this paper, for the small, important example of inflammation modeling a network is constructed and different learning algorithms are proposed. It turned out that due to the nonlinear dynamics evolutionary approaches are necessary to fit the parameters for sparse, given data. Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence - ICTAI 2003
In bioinformatics, biochemical pathways can be modeled by many differential equations. It is still an open problem how to fit the huge amount of parameters of the equations to the available data. Here, the approach of systematically learning the parameters is necessary. In this paper, for the small, important example of inflammation modeling a network is constructed and different learning algorithms are proposed. It turned out that due to the nonlinear dynamics evolutionary approaches are necessary to fit the parameters for sparse, given data. Keywords: model parameter adaption, septic shock. coupled differential equations, genetic algorithm.
In contrast to the symbolic approach, neural networks seldom are designed to explain what they have learned. This is a major obstacle for its use in everyday life. With the appearance of neuro-fuzzy systems which use vague, human-like categories the situation has changed. Based on the well-known mechanisms of learning for RBF networks, a special neuro-fuzzy interface is proposed in this paper. It is especially useful in medical applications, using the notation and habits of physicians and other medically trained people. As an example, a liver disease diagnosis system is presented.
The prevention of credit card fraud is an important application for prediction techniques. One major obstacle for using neural network training techniques is the high necessary diagnostic quality: Since only one financial transaction of a thousand is invalid no prediction success less than 99.9% is acceptable. Due to these credit card transaction proportions complete new concepts had to be developed and tested on real credit card data. This paper shows how advanced data mining techniques and neural network algorithm can be combined successfully to obtain a high fraud coverage combined with a low false alarm rate.
This paper describes the use of a Radial Basis Function (RBF) neural network in the approximation of process parameters for the extrusion of a rubber profile in tyre production. After introducing the rubber industry problem, the RBF network model and the RBF net learning algorithm are developed, which uses a growing number of RBF units to compensate the approximation error up to the desired error limit. Its performance is shown for simple analytic examples. Then the paper describes the modelling of the industrial problem. Simulations show good results, even when using only a few training samples. The paper is concluded by a discussion of possible systematic error influences, improvements and potential generalisation benefits. Keywords: Adaptive process control; Parameter estimation; RBF-nets; Rubber extrusion
In this paper we regard first the situation where parallel channels are disturbed by noise. With the goal of maximal information conservation we deduce the conditions for a transform which "immunizes" the channels against noise influence before the signals are used in later operations. It shows up that the signals have to be decorrelated and normalized by the filter which corresponds for the case of one channel to the classical result of Shannon. Additional simulations for image encoding and decoding show that this constitutes an efficient approach for noise suppression. Furthermore, by a corresponding objective function we deduce the stochastic and deterministic learning rules for a neural network that implements the data orthonormalization. In comparison with other already existing normalization networks our network shows approximately the same in the stochastic case but, by its generic deduction ensures the convergence and enables the use as independent building block in other contexts, e.g. whitening for independent component analysis. Keywords: information conservation, whitening filter, data orthonormalization network, image encoding, noise suppression.
This paper describes the use of a radial basis function (RBF) neural network. It approximates the process parameters for the extrusion of a rubber profile used in tyre production. After introducing the problem, we describe the RBF net algorithm and the modeling of the industrial problem. The algorithm shows good results even using only a few training samples. It turns out that the „curse of dimensions“ plays an important role in the model. The paper concludes by a discussion of possible systematic error influences and improvements.
The paper focuses on the division of the sensor field into subsets of sensor events and proposes the linear transformation with the smallest achievable error for reproduction: the transform coding approach using the principal component analysis (PCA). For the implementation of the PCA, this paper introduces a new symmetrical, lateral inhibited neural network model, proposes an objective function for it and deduces the corresponding learning rules. The necessary conditions for the learning rate and the inhibition parameter for balancing the crosscorrelations vs. the autocorrelations are computed. The simulation reveals that an increasing inhibition can speed up the convergence process in the beginning slightly. In the remaining paper, the application of the network in picture encoding is discussed. Here, the use of non-completely connected networks for the self-organized formation of templates in cellular neural networks is shown. It turns out that the self-organizing Kohonen map is just the non-linear, first order approximation of a general self-organizing scheme. Hereby, the classical transform picture coding is changed to a parallel, local model of linear transformation by locally changing sets of self-organized eigenvector projections with overlapping input receptive fields. This approach favors an effective, cheap implementation of sensor encoding directly on the sensor chip. Keywords: Transform coding, Principal component analysis, Lateral inhibited network, Cellular neural network, Kohonen map, Self-organized eigenvector jets.
After a short introduction into traditional image transform coding, multirate systems and multiscale signal coding the paper focuses on the subject of image encoding by a neural network. Taking also noise into account a network model is proposed which not only learns the optimal localized basis functions for the transform but also learns to implement a whitening filter by multi-resolution encoding. A simulation showing the multi-resolution capabilitys concludes the contribution.
We present a framework for the self-organized formation of high level learning by a statistical preprocessing of features. The paper focuses first on the formation of the features in the context of layers of feature processing units as a kind of resource-restricted associative multiresolution learning We clame that such an architecture must reach maturity by basic statistical proportions, optimizing the information processing capabilities of each layer. The final symbolic output is learned by pure association of features of different levels and kind of sensorial input. Finally, we also show that common error-correction learning for motor skills can be accomplished also by non-specific associative learning. Keywords: feedforward network layers, maximal information gain, restricted Hebbian learning, cellular neural nets, evolutionary associative learning
It is well known that artificial neural nets can be used as approximators of any continuous functions to any desired degree and therefore be used e.g. in high - speed, real-time process control. Nevertheless, for a given application and a given network architecture the non-trivial task remains to determine the necessary number of neurons and the necessary accuracy (number of bits) per weight for a satisfactory operation which are critical issues in VLSI and computer implementations of nontrivial tasks. In this paper the accuracy of the weights and the number of neurons are seen as general system parameters which determine the maximal approximation error by the absolute amount and the relative distribution of information contained in the network. We define as the error-bounded network descriptional complexity the minimal number of bits for a class of approximation networks which show a certain approximation error and achieve the conditions for this goal by the new principle of optimal information distribution. For two examples, a simple linear approximation of a non-linear, quadratic function and a non-linear approximation of the inverse kinematic transformation used in robot manipulator control, the principle of optimal information distribution gives the the optimal number of neurons and the resolutions of the variables, i.e. the minimal amount of storage for the neural net. Keywords: Kolmogorov complexity, e-Entropy, rate-distortion theory, approximation networks, information distribution, weight resolutions, Kohonen mapping, robot control.
Clathrates are candidate materials for thermoelectric applications because of a number of unique properties. The clathrate I phases in the Ba-Ni-Ge ternary system allow controlled variation of the charge carrier concentration by adjusting the Ni content. Depending on the Ni content, the physical properties vary from metal-like to insulator-like and show a transition from p-type to n-type conduction. Here we present first results on the characterization of millimeter-sized single crystals grown by the Bridgman technique. Single crystals with a composition of Ba8Ni3.5Ge42.1h0.4 show metallic behavior (dp/dT > 0) albeit with high resistivity at room temperature [p (300 K) = 1 mOhm cm]. The charge carrier concentration at 300 K, as determined from Hall-effect measurements, is 2.3 e-/unit cell. The dimensionless thermoelectric figure of merit estimated at 680 K is ZT ~ 0.2. Keywords Clathrates - thermoelectric material - intermetallic compound - nickel
We suggest a new method to compute the spectrum and wave functions of excited states. We construct a stochastic basis of Bargmann link states, drawn from a physical probability density distribution and compute transition amplitudes between stochastic basis states. From such transition matrix we extract wave functions and the energy spectrum. We apply this method toU(1)2+1 lattice gauge theory. As a test we compute the energy spectrum, wave functions and thermodynamical functions of the electric Hamiltonian and compare it with analytical results. We find excellent agreement. We observe scaling of energies and wave functions in the variable of time. We also present first results on a small lattice for the full Hamiltonian including the magnetic term.
Central elements of the Bologna declaration have been implemented in a huge variety of curricula in humanities, social sciences, natural sciences and engineering sciences at German universities. Overall the results have been nothing less than disastrous. Surprisingly, this seems to be the perfect time for German universities to talk about introducing a curriculum that is fully compatible with the Bologna declaration for medical education as well. However, German medical education does not have problems the Bologna declaration is intended to solve, such as quality, mobility, internationalization and employability. It is already in the Post-Bologna age.
Nucleation experiments starting from the reaction of OH radicals with SO2 have been performed in the IfT-LFT flow tube under atmospheric conditions at 293±0.5 K for a relative humidity of 13–61%. The presence of different additives (H2, CO, 1,3,5-trimethylbenzene) for adjusting the OH radical concentration and resulting OH levels in the range (4–300) ×105 molecule cm -3 did not influence the nucleation process itself. The number of detected particles as well as the threshold H2SO4 concentration needed for nucleation was found to be strongly dependent on the counting efficiency of the used counting devices. High-sensitivity particle counters allowed the measurement of freshly nucleated particles with diameters down to about 1.5 nm. A parameterization of the experimental data was developed using power law equations for H2SO4 and H2O vapour. The exponent for H2SO4 from different measurement series was in the range of 1.7–2.1 being in good agreement with those arising from analysis of nucleation events in the atmosphere. For increasing relative humidity, an increase of the particle number was observed. The exponent for H2O vapour was found to be 3.1 representing an upper limit. Addition of 1.2×1011 molecule cm -3 or 1.2×1012 molecule cm -3 of NH3 (range of atmospheric NH3 peak concentrations) revealed that NH3 has a measureable, promoting effect on the nucleation rate under these conditions. The promoting effect was found to be more pronounced for relatively dry conditions, i.e. a rise of the particle number by 1–2 orders of magnitude at RH = 13% and only by a factor of 2–5 at RH = 47% (NH3 addition: 1.2×1012 molecule cm -3). Using the amine tert-butylamine instead of NH3, the enhancing impact of the base for nucleation and particle growth appears to be stronger. Tert-butylamine addition of about 1010 molecule cm -3 at RH = 13% enhances particle formation by about two orders of magnitude, while for NH3 only a small or negligible effect on nucleation in this range of concentration appeared. This suggests that amines can strongly influence atmospheric H2SO4-H2O nucleation and are probably promising candidates for explaining existing discrepancies between theory and observations.