José D. Piñeiro

Patient-centered simulation tool for aiding in hospital management

Abstract The study of a particular complex system by means of computer simulation is described in this paper. Hospitals are chosen as target systems where the proposed methodology is applied. In order to choose the right decisions, hospital managers need all the information about the functioning of the organization. This research project presents a simulation tool that allows virtual societies such as hospitals to be implemented. In this way, the study of emergent behaviors in these systems can be carried out. The methodology used to model the hospital is process oriented. This approach allows us to implement a patient-centered simulation tool.

Using KADS methodology in a simulation assisted knowledge based system: application to hospital management

Abstract This paper presents a knowledge-based system for aiding in the decision-making process that is carried out in hospital management. There are a number of reasons that have led us to choose a tool such as this one: the amount of information generated in a hospital, its great interrelation and the need of heuristic knowledge for its processing. The KBS has been designed following the KADS methodology. KADS has allowed us to obtain a structured representation of the knowledge, which makes easier both the construction and the debugging of the knowledge base. As a starting point, the decision-making task has been decomposed in four subtasks: monitoring; diagnosis; prediction of the possible solutions for the stated problem; and design of the solution. The prediction task can only be performed through a simulation program where the dynamics of the hospital is modeled. This allows the system to detect the consequences of the application of different possible solutions. The co-operation between simulation and artificial intelligence has proven to be an adequate technique for dealing with the decision-making tasks that are involved with the management of complex organizations.

Patient-Centered Simulation to Aid Decision-Making in Hospital Management:

Computer simulation has eased the study of complex systems. A hospital is a complex sys tem that is formed by a large number of units with strong interrelationships. Even though resources are limited, patients must be effi ciently treated. This paper presents simulation as a tool to aid hospital management. In the first phase we present an introduction to the problem and its motivation. The next step is the description of how the system functions. The choice of the simulation model and the approach in dealing with it are described. Finally, the implementation of the simulation tool is pre sented. This tool is used for supporting the deci sion-making processes in hospital management.

An expert system for supervised classifier design: Application to Alzheimer diagnosis

The aim of this paper is to present a knowledge-based approach to supervised classifier design. For this purpose, an expert system has been built following the Common KADS methodology. Classifier design is seen as a general design problem and a modified version of the well-known Propose-Critique-Modify method is proposed as a suitable strategy to solve it. In this context, a number of heuristics are used to shrink the search in the space of possible designs. Although the system is evaluated on different datasets, special emphasis is put on a particular problem: Alzheimers diagnosis.

Automatic analysis of signals with symbolic content

This paper presents a set of methods for helping in the analysis of signals with particular features that admit a symbolic description. The methodology is based on a general discrete model for a symbolic processing subsystem, which is fuzzyfied by means of a fuzzy inference system. In this framework a number of design problems have been approached. The curse of dimensionality problem and the specification of adequate membership functions are the main ones. In addition, other strategies, which make the design process simpler and more robust, are introduced. Their goals are automating the production of the rule base of the fuzzy system and composing complex systems from simpler subsystems under symbolic constrains. These techniques are applied to the analysis of wakefulness episodes in the sleep EEG. In order to solve the practical difficulty of finding remarkable situations from the outputs of the symbolic subsystems an unsupervised adaptive learning technique (FART network) has been applied.

Stochastic optimal controllers for a DC servo motor: Applicability and performance

Abstract In this work some optimal control algorithms have been designed for and implemented in a real plant. The plant is a DC motor, controlled in the armature. Both deterministic and stochastic control policies have been developed. The aim of this paper is to show the applicability of optimal control algorithms to improve performances, to look for an empirical proof of the effects that appear in a stochastic policy (such as the caution effect), and to confirm an increment in the performance in the stochastic algorithms as compared with entirely deterministic algorithms. The control schemes applied are Dynamic Programming and Generalized Predictive Control.

Using neural networks to improve classification: application to brain maturation

The knowledge acquisition problem is one of the most difficult issues in elaborating a medical expert system. This is more true in the context of automated brain signal diagnosis. This kind of knowledge does not lend itself to be represented in a classical rule-based system and is not easily put in quantitative terms by the specialists. Artificial neural networks (ANNs) provide a useful alternative for capturing this information. In this work, an application of ANNs to brain maturation prediction is presented. The problem is essentially a supervised classification. A case data base consisting of data extracted from electroencephalographic (EEG) signals and diagnoses carried out by an expert neurologist serves to test the ability of several statistical classifiers and several kinds of ANNs in reproducing the expert results. There is also a discussion on how to integrate ANNs in a higher-level knowledge-based system for brain signal interpretation.

Application of time dependent probabilistic collision state checkers in highly dynamic environments.

When computing the trajectory of an autonomous vehicle, inevitable collision states must be avoided at all costs, so no harm comes to the device or pedestrians around it. In dynamic environments, considering collisions as binary events is risky and inefficient, as the future position of moving obstacles is unknown. We introduce a time-dependent probabilistic collision state checker system, which traces a safe route with a minimum collision probability for a robot. We apply a sequential Bayesian model to calculate approximate predictions of the movement patterns of the obstacles, and define a time-dependent variation of the Dijkstra algorithm to compute statistically safe trajectories through a crowded area. We prove the efficiency of our methods through experimentation, using a self-guided robotic device.

Stability of Quasi-Periodic Orbits in Recurrent Neural Networks

A simple discrete recurrent neural network model is considered. The local stability is analyzed with the associated characteristic model. In order to study the dynamic behavior of the quasi-periodic orbit, it is necessary to determine the Neimark-Sacker bifurcation. In the case of two neurons, one necessary condition that yields the Neimark-Sacker bifurcation is found. In addition to this, the stability and direction of the Neimark-Sacker bifurcation are determined by applying normal form theory and the center manifold theorem. An example is given and a numerical simulation is performed to illustrate the results. The phase-locking phenomena are analyzed for certain experimental results with Arnold Tongues in a particular weight configuration.

Experiments on A D.C. Motor Based System for a Digital Control Course

Experiments on a d.c. motor-based system for a digital control course A set of real-time experiments is presented. These experiments are implemented on a position-velocity system and cover a wide range of control techniques. We start with basic control actions and continue with more complex strategies like optimal control. We emphasize the study of all practical aspects of the experiences: noise, delays, etc.