A novel approach for generating full Electronic Medical Records of synthetic victims is described. Special emphasis is put on the data mining steps that build patient care models and perform clustering of this highly dimensional data set. A methodology for cluster validation is proposed. Results for a large data set with Staphylococcus aureus and Methicillin-Resistant Staphylococcus aureus infections are presented.

Efficient data retrieval from large databases and the World Wide Web is an important task that has to be performed routinely in a wide range of applications. To facilitate the drug discovery process, the biomedical community needs tools that enable fast searching of databases and the Web. SmartPortal assists users in their searches of biomedical information by quickly finding results of particular interest to the user in the deluge of data and the moving them to the top of the results list. SmartPortal achieves its goal through 1) constructing a user model for each particular user that captures the type of information of interest to that user; 2) using machine learning technologies to adapt the model to changing user needs, and to learn from user feedback what type of information is of interest to the user at any given moment; 3) automatic query expansion (using ontologies) to help the user construct useful queries faster and retrieve pertinent information

In this paper, we introduce the concept of switching from one supervisory control to another one when changes in the state of an embedded sensory network occur. The sensory devices are assumed to be single functional, that is, they are programmed to observe and report only one event. The switching model is built based on the two criteria of language observability and state synchronization. Switching model is the basis for a megacontroller, which monitors the state of the sensory network embedded in the plant as well as the state of the supervisor in charge. Every time the state of this network changes, the megacontroller reconfigures the control system by finding an appropriate supervisor.

This paper describes a Genetic Algorithm (GA) convergence study for a highly multi-modal fitness function with non-ordered parameters. The measures of GA performance used are best single solution performance, effectiveness in finding the optimum and percentage of total search space (PTSS) covered. We developed several ways of adapting the crossover and mutation probabilities, and we compare the results of these methods with a canonical GA, a mutation-only GA, and the Srinivas' adaptive method. The results indicate that a large constant probability of crossover, regardless of the mutation method used does not provide high efficiency, for medium and large populations if covering a small PTSS. The most effective method while covering the smallest PTSS, is an adaptive mutation-only method. Our results suggest that when convergence speed is of utmost interest, for functions with non-ordered parameters mutation is more important than crossover despite massive multi-modality of the function optimized. Methods with adaptive crossover can, however, also give good results as long as mutation with a constant high probability is also performed.