El proceso de planificación del alta en centros de rehabilitación: sistemas de información para la evaluación de pacientes

IV Congreso Nacional de Informática de la Salud; 2001 Mar 28-30; Madrid; organizado por la Sociedad Española de Informática de la Salud[Resumen] La planificación del alta es un proceso que debe comenzar desde el mismo momento del ingreso. Debe ser sistemático, interdisciplinario y coordinado por un especialista sanitario. Debe involucrar al paciente y a su familia e incluir la valoración de su entorno de vida, soporte familiar, valoración de la discapacidad y posibilidades de llevar a cabo una rehabilitación vocacional. Todas las decisiones que se tomen en el proceso del alta deben implicar y reflejar el consenso de la familia y el propio paciente con el equipo médico (1). Por lo tanto, surge la necesidad de utilizar algún sistema de medición de incapacidad, dada la gran variedad de patologías que abarca la Medicina Física y Rehabilitación y la existencia de cuadros nosológicos muy diferentes en cuanto a su etiología, gravedad y pronóstico. En este contexto, se hace necesaria la utilización de escalas de valoración funcional, que simplifiquen este trabajo y posibiliten un mayor control de todo el proceso desde su inicio. Existen múltiples escalas de valoración, tanto específicas como de propósito general, siendo la más utilizada la Functional Independence Measure (FIM) de la Uniform Data System for Medical Rehabilitation. A partir de estas escalas se han desarrollado diferentes sistemas de datos: el Union Data System (UDS) y el TBI Model Systems National Database del National Institute on Disability and Rehabilitation Research entre los más destacados. En este artículo, además de exponer las distintas fases del proceso de planificación del alta, se hará un estudio de los distintos sistemas de información desarrollados, así como de las escalas de valoración utilizadas

Desarrollo de una metodología e implementación de un sistema basado en el conocimiento de filosofía híbrida : una aplicación para la evaluación de impacto ambiental

[Resumen] El avance producido en los últimos años en el ámbito de la computación e inteligencia artificial permite disponer de un conjunto de técnicas que traten, de una forma experta y consistente, el amplio y complejo dominio de las evaluaciones de impacto ambiental. Las características generales del dominio, de gran tamaño y con una tasa de cambio de los conocimientos elevada, sugiere como adecuada una aproximación de integración entre bases de datos, sistemas expertos y redes de neuronas artificiales. La ausencia de una metodología de integración entre las citadas técnicas, requiere un amplio estudio de diversas metodologías existentes en cada una de las técnicas involucradas, necesario para la consecución de una metodología de integración que incorpore un alto gradod e integridad y consistencia a los sistemas de información resultantes. En general, las capacidades de aprendizaje de las redes de neuronas artificiales unidas a las posibilidades de razonamiento y explicación de los sistemas expertos y a las capacidades de definición y manipulación de grandes cantidades de datos de las bases de datos permiten manejar el complejo dominio de las evaluaciones de impacto ambiental con unas mínimas garantias. En el aspecto técnico, el sistema general incorpora un nivel de abstracción superior basado en reglas de producción que define las hipótesis generales del dominio (acciones causantes de impacto, cruces de efectos y medidas correctoras), y un nivel inferior, con un mecanismo de razonamiento basado en restricciones, que decremente el coste de mantenimiento del sistema. Además, se incorpora una nueva arquitectura de manipulación de redes de neuronas artificiales que considera la integración de las diferentes etapas de los sistemas conexionistas (arquitectoras, conjuntos de entrenamiento, conjuntos de test y en ejecución)

Framework of fully integrated hybrid systems

The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-011-0672-9A framework of fully integrated hybrid systems (HSs) is proposed for the development and management of HS which involve databases, advanced user interfaces, symbolic systems, and artificial neural networks. This framework provides a common input–output interface among those HS modules developed on the framework, with a completely two-directional flow control and a highly parallel processing. This integration framework facilitates the incorporation of heterogeneous modules, together with their subsequent management and updating

Self-tuning of disk input–output in operating systems

The final publication is available via http://dx.doi.org/10.1016/j.jss.2011.07.030One of the most difficult and hard to learn tasks in computer system management is tuning the kernel parameters in order to get the maximum performance. Traditionally, this tuning has been set using either fixed configurations or the subjective administrator's criteria. The main bottleneck among the subsystems managed by the operating systems is disk input/output (I/O). An evolutionary module has been developed to perform the tuning of this subsystem automatically, using an adaptive and dynamic approach. Any computer change, both at the hardware level, and due to the nature of the workload itself, will make our module adapt automatically and in a transparent way. Thus, system administrators are released from this kind of task and able to achieve some optimal performances adapted to the framework of each of their systems. The experiment made shows a productivity increase in 88.2% of cases and an average improvement of 29.63% with regard to the default configuration of the Linux operating system. A decrease of the average latency was achieved in 77.5% of cases and the mean decrease in the request processing time of I/O was 12.79%

Study of classical conditioning in Aplysia through the implementation of computational models of its learning circuit

“This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Experimental & Theoretical Artificial Intelligence on 04 Jul 2007, available online: http://wwww.tandfonline.com/DOI:10.1080/09528130601052177.”The learning phenomenon can be analysed at various levels, but in this paper we treat a specific paradigm of artificial intelligence, i.e. artificial neural networks (ANNs), whose main virtue is their capacity to seek unified and mutually satisfactory solutions which are relevant to biological and psychological models. Many of the procedures and methods proposed previously have used biological and/or psychological principles, models, and data; here, we focus on models which look for a greater degree of coherence. Therefore we analyse and compare all aspects of the Gluck–Thompson and Hawkins ANN models. A multithread computer model is developed for analysis of these models in order to study simple learning phenomena in a marine invertebrate (Aplysia californica) and to check their applicability to research in psychology and neurobiology. The predictive capacities of the models differs significantly: the Hawkins model provides a better analysis of the behavioural repertory of Aplysia on both the associative and the non-associative learning level. The scope of the ANN modelling technique is broadened by integration with neurobiological and behavioural models of associative learning, allowing enhancement of some architectures and procedures that are currently being used

Artificial Neural Networks Manipulation Server: Research on the Integration of Databases and Artificial Neural Networks

The final publication is available at Springer via http://dx.doi.org/10.1007/s005210200011This paper proposes a new whole and distributed integration approach between Artificial Neural Networks (ANNs) and Databases (DBs) taking into account the different stages of the former’s lifecycle (training, test and running). The integration architecture which has been developed consists of an ANN Manipulation Server (AMS) based on a client-server approach, which improves the ANNs’ manipulation and experimentation capabilities considerably, and also those of their training and test sets, together with their modular reuse among possibly remote applications. Moreover, the chances of integrating ANNs and DBs are analysed, proposing a new level of integration which improves the integration features considerably. This level has not been contemplated yet at full reach in any of the commercial or experimental tools analysed up to the present date. Finally, the application of the integration architecture which has been developed to the specific domain of Environmental Impact Assessments (EIAs) is studied. Thus, the versatility and efficacy of that architecture for developing ANNs is tested. The enormous complexity of the functioning of the patterns which rule the environment’s behaviour, and the great number of variables involved, make it the ideal domain for experimenting on the application of ANNs together with DBs

EEIE: an expert system for environmental impact evaluation

The environment has a very important role in public health (i.e: cardiovascular pathology, discomfort, ...). Doing and appropiate Environmental Impact Evaluation of man actions will help to preserve public health of possible harmfull effects of these actions. Environmental Impact Evaluation using Leopold and Battelle methods. has been implemented in the EEIE expert system. It's an efficient system to suggest, characterize and evaluate hypothesis associated with environmental impact. These hypothesis are stablished in order to link project actions with environmental factors, as well as to determine the involucrate corrective actions. The set of hypothesis obtained is grouped together one obtained by a back propagation artificial neural network (ANN) and then EEIE evaluate every hypothesis. Therefore, EEIE presented here generalize expert knowledge of the environmental impact domain