A new method for diagnosing multiple diseases in large medical decision support systems based on causal probabilistic networks is proposed. The method is based on characteristics of the diagnostic process that we believe to be present in many diagnostic tasks, both inside and outside medicine. The diagnosis must often be made under uncertainty, choosing between diagnoses that each have small prior probabilities, but not so small that the possibility of two or more simultaneous diseases can be ignored. Often a symptom can be caused by several diseases and the presence of several diseases tend to aggravate the symptoms. For diagnostic problems that share these characteristic, we have proposed a method that operates in a number of phases: in the first phase only single diseases are considered and this helps to focus the attention on a smaller number of plausible diseases. In the second phase, pairs of diseases are considered, which make it possible to narrow down the field of plausible diagnoses further. In the following phases, larger subsets of diseases are considered. The method was applied to the diagnosis of neuromuscular disorders, using previous experience with the so-called MUNIN system as a starting point. The results showed that the method gave large reductions in computation time without compromising the computational accuracy in any substantial way. It is concluded that the method enables practical inference in large medical expert systems based on causal probabilistic networks.
Our goal is to get better understanding of different kind of dependencies behind the high-level capability areas. The models are suitable for investigating present state capabilities or future developments of capabilities in the context of technology forecasting. Three levels are necessary for a model describing effects of technologies on military capabilities. These levels are capability areas, systems and technologies. The contribution of this paper is to present one possible model for interdependencies between technologies. Modelling interdependencies between technologies is the last building block in constructing a quantitative model for technological forecasting including necessary levels of abstraction. This study supplements our previous research and as a result we present a model for the whole process of capability modelling. As in our earlier studies, capability is defined as the probability of a successful task or operation or proper functioning of a system. In order to obtain numerical data to demonstrate our model, we conducted a questionnaire to a group of defence technology researchers where interdependencies between seven representative technologies were inquired. Because of a small number of participants in questionnaires and general uncertainties concerning subjective evaluations, only rough conclusions can be made from the numerical results.
Based on a review of different analytical frameworks, it is suggested to run a table top cyber wargame when trying to analyse the effects of closed national networks being imposed in the near future. The scope of the wargame is to extract results to show how the resilience of an open national network differs from a closed national network. It is self-evident that the formation process of resilience is different between the diverse systems. The proposed wargame is a two-sided cyber table top wargame. The wargame is based on at least two blue teams, at least one red team and a control team (namely a white team). One blue team is located in the closed national networks and its system relies on closed national network infrastructure. The other blue team operates its system within open network society. By designing, constructing and executing the proposed cyber wargame we argue it is possible to find these differences and similarities as well. Current research improves cyber situation awareness and proposes a direction to be followed when trying to understand the changing circumstances of the cyber space. It also suggests how the research resources could be directed when trying to improve the situation awareness of the closing process.
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