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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 %).
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. Keywords: model parameter adaption, septic shock. coupled differential equations, genetic algorithm.
It is shown that between one-turn pushdown automata (1-turn PDAs) and deterministic finite automata (DFAs) there will be savings concerning the size of description not bounded by any recursive function, so-called non-recursive tradeoffs. Considering the number of turns of the stack height as a consumable resource of PDAs, we can show the existence of non-recursive trade-offs between PDAs performing k+ 1 turns and k turns for k >= 1. Furthermore, non-recursive trade-offs are shown between arbitrary PDAs and PDAs which perform only a finite number of turns. Finally, several decidability questions are shown to be undecidable and not semidecidable.
Zellularautomaten sind ein massiv paralleles Berechnungsmodell, das aus sehr vielen identischen einfachen Prozessoren oder Zellen besteht, die homogen miteinander verbunden sind und parallel arbeiten. Es gibt Zellularautomaten in unterschiedlichen Ausprägungen. Beispielsweise unterscheidet man die Automaten nach der zur Verfügung stehenden Zeit, nach paralleler oder sequentieller Verarbeitung der Eingabe oder durch Beschränkungen der Kommunikation zwischen den einzelnen Zellen. Benutzt man Zellularautomaten zum Erkennen formaler Sprachen und betrachtet deren generative Mächtigkeit, dann kann bereits das einfachste zellulare Modell kontextsensitive Sprachen akzeptieren. In dieser Arbeit wird die Beschreibungskomplexität von Zellularautomaten betrachtet. Es wird untersucht, wie sich die Beschreibungsgröße einer formalen Sprache verändern kann, wenn die Sprache mit unterschiedlichen Typen von Zellularautomaten oder sequentiellen Modellen beschrieben wird. Ein wesentliches Ergebnis im ersten Teil der Arbeit ist, daß zwischen zwei Automatenklassen, deren entsprechende Sprachklassen echt ineinander enthalten oder unvergleichbar sind, nichtrekursive Tradeoffs existieren. Das heißt, der Größenzuwachs beim Wechsel von einem Automatenmodell in das andere läßt sich durch keine rekursive Funktion beschränken. Im zweiten Teil der Arbeit werden Zellularautomaten dahingehend beschränkt, daß nur eine feste Zellenzahl zugelassen ist. Zusätzlich werden Automaten mit unterschiedlichem Grad an bidirektionaler Kommunikation zwischen den einzelnen Zellen betrachtet, und es wird untersucht, welche Auswirkungen auf die Beschreibungsgröße unterschiedliche Grade an bidirektionaler Kommunikation haben können. Im Gegensatz zum unbeschränkten Modell können polynomielle und damit rekursive obere Schranken bei Umwandlungen zwischen den einzelnen Modellen bewiesen werden. Durch den Beweis unterer Schranken kann in fast allen Fällen auch die Optimalität der Konstruktionen belegt werden.
Despite a legal framework being in place for several years, the market share of qualified electronic signatures is disappointingly low. Mobile Signatures provide a new and promising opportunity for the deployment of an infrastructure for qualified electronic signatures. We analyzed two possible signing approaches (server based and client based signatures) and conclude that SIM-based signatures are the most secure and convenient solution. However, using the SIM-card as a secure signature creation device (SSCD) raises new challenges, because it would contain the user’s private key as well as the subscriber identification. Combining both functions in one card raises the question who will have the control over the keys and certificates. We propose a protocol called Certification on Demand (COD) that separates certification services from subscriber identification information and allows consumers to choose their appropriate certification services and service providers based on their needs. We also present some of the constraints that still have to be addressed before qualified mobile signatures are possible.