Diseases of the digestive system and a system for patient classification: diagnostic-related groups (DRG)

In the last years different classification systems for hospitalized patients have been developed in the United States, that allow to evaluate different aspects of any Hospital: The quality, the efficacy and the efficiency. The classification of "The Diagnosis Related Groups" (DRG) relates the hospitalized patients with the expenses that they generate, and it is the most used in Europe. In the version DRG-All Patient, all the diagnoses and procedures of the hospitalized patients, using the International Classification of Diseases, are divided in to 25 Major Diagnostic Categories (MDC) where each one excludes all the others. We describe the behavior with respect to the age, sex and stay of all the GDR integrated in the CDM 6 and 7: diseases and disorders of the digestive tract, and diseases and disorders of the hepatobiliary system and the pancreas. The study has been carried out with a national data base of two millions of cases seen from 214 acute-care Hospitals. The MDC number 6 is the most frequent, with an important number of patients in the pediatric age; it is more frequent in males; the average stay is 8.59 days and 19 GDR (36.5%) have a variation coefficient greater than one. The other MDC, number 7 is less frequent, the majority of the patients are between 60-80 years of age, the average stay is 12.2 days with a coefficient of variation greater than one in 4 GDR (17.4%). Clinical Services should know the characteristics and behavior of their patients, as well as the comparison with the national data bases. This may allow a control of the quality and the costs by using a common "language" with the managers.     

Automatic metro map layout using multicriteria optimization

This paper describes an automatic mechanism for drawing metro maps. We apply multicriteria optimization to find effective placement of stations with a good line layout and to label the map unambiguously. A number of metrics are defined, which are used in a weighted sum to find a fitness value for a layout of the map. A hill climbing optimizer is used to reduce the fitness value, and find improved map layouts. To avoid local minima, we apply clustering techniques to the map-the hill climber moves both stations and clusters when finding improved layouts. We show the method applied to a number of metro maps, and describe an empirical study that provides some quantitative evidence that automatically-drawn metro maps can help users to find routes more efficiently than either published maps or undistorted maps. Moreover, we have found that, in these cases, study subjects indicate a preference for automatically-drawn maps over the alternatives.     

Data-driven latent-variable model-based predictive control for continuous processes

A model-based predictive control methodology in the space of the latent variables for continuous processes is presented. Implementing identification and control in the latent variable space eases identification in the case of correlation in the data set, acts as a prefilter reducing the effect of noisy data, and reduces computational complexity. The proposed data-driven LV-MPC approach deals with setting the control horizon different to the prediction horizon, improves Hessian conditioning, and attains offset-free tracking. Additionally, a weighting matrix is introduced in the identification stage so that the performance of the predictor in the near horizon can be enhanced. A MIMO example shows how the proposed methodology can outperform conventional data-driven MPC in terms of computational complexity and reference tracking.     

Deployment algorithms for a power‐constrained mobile sensor network

This paper presents distributed coverage algorithms for mobile sensor networks in which agents have limited power to move. Rather than making use of a constrained optimization technique, our approach accounts for power constraints by assigning non‐homogeneously time‐varying regions to each robot. This leads to novel partitions of the environment into limited‐range, generalized Voronoi regions. The motion control algorithms are then designed to ascend the gradient of several types of locational optimization functions. In particular, the objective functions reflect the global energy available to the group and different coverage criteria. As we discuss in the paper, this has an effect on limiting each agent's velocity to save energy and balance its expenditure across the network. Copyright © 2009 John Wiley & Sons, Ltd.     

TRIMS,a privacy-aware trust and reputation model for identity management systems

Electronic transactions are becoming more important everyday. Several tasks like buying goods, booking flights or hotel rooms, or paying for streaming a movie, for instance, can be carried out through the Internet. Nevertheless, they are still some drawbacks due to security threats while performing such operations. Trust and reputation management rises as a novel way of solving some of those problems. In this paper we present our work TRIMS (a privacy-aware trust and reputation model for identity management systems), which applies a trust and reputation model to guarantee an acceptable level of security when deciding if a different domain might be considered reliable when receiving certain sensitive user’s attributes. Specifically, we will address the problems which surfaces when a domain needs to decide whether to exchange some information with another possibly unknown domain to effectively provide a service to one of its users. This decision will be determined by the trust deposited in the targeting domain. As far as we know, our proposal is one of the first approaches dealing with trust and reputation management in a multi-domain scenario. Finally, the performed experiments have demonstrated the robustness and accuracy of our model in a wide variety of scenarios.     

A multi-granular linguistic model for management decision-making in performance appraisal

The performance appraisal is a relevant process to keep and improve the competitiveness of companies in nowadays. In spite of this relevance, the current performance appraisal models are not sufficiently well-defined either designed for the evaluation framework in which they are defined. This paper proposes a performance appraisal model where the assessments are modelled by means of linguistic information provided by different sets of reviewers in order to manage the uncertainty and subjectivity of such assessments. Therefore, the reviewers could express their assessments in different linguistic scales according to their knowledge about the evaluated employees, defining a multi-granular linguistic evaluation framework. Additionally, the proposed model will manage the multi-granular linguistic labels provided by appraisers in order to compute collective assessments about the employees that will be used by the management team to make the final decision about them.     

A new fast prototype selection method based on clustering

In supervised classification, a training set T is given to a classifier for classifying new prototypes. In practice, not all information in T is useful for classifiers, therefore, it is convenient to discard irrelevant prototypes from T. This process is known as prototype selection, which is an important task for classifiers since through this process the time for classification or training could be reduced. In this work, we propose a new fast prototype selection method for large datasets, based on clustering, which selects border prototypes and some interior prototypes. Experimental results showing the performance of our method and comparing accuracy and runtimes against other prototype selection methods are reported.     

Real-time massive convolution for audio applications on GPU

Massive convolution is the basic operation in multichannel acoustic signal processing. This field has experienced a major development in recent years. One reason for this has been the increase in the number of sound sources used in playback applications available to users. Another reason is the growing need to incorporate new effects and to improve the hearing experience. Massive convolution requires high computing capacity. GPUs offer the possibility of parallelizing these operations. This allows us to obtain the processing result in much shorter time and to free up CPU resources. One important aspect lies in the possibility of overlapping the transfer of data from CPU to GPU and vice versa with the computation, in order to carry out real-time applications. Thus, a synthesis of 3D sound scenes could be achieved with only a peer-to-peer music streaming environment using a simple GPU in your computer, while the CPU in the computer is being used for other tasks. Nowadays, these effects are obtained in theaters or funfairs at a very high cost, requiring a large quantity of resources. Thus, our work focuses on two mains points: to describe an efficient massive convolution implementation and to incorporate this task to real-time multichannel-sound applications.     

Formal model for assigning human resources to teams in software projects

Context

Human resources play a critical role in software project success. However, people are still the least formalized factor in today’s process models. Generally, people are assigned to roles and project teams are formed on the basis of project leaders’ experience of people, constraints (e.g. availability) and skill requirements. Yet this process has to take multiple factors into account. Few works in the literature model this process. Most of these are informal proposals focusing on the individual assignment of people to project tasks and do not consider other aspects like team formation as a whole.

Objective

In this paper we formulate a formal model for assigning human resources to software project teams. Additionally, we describe the key results of the knowledge management process enacted to output the elements of the model.

Method

The model elements were identified using the Delphi expert consultation method and applying psychological tests. The proposed model was implemented in a software tool and validated on two software development organization assignment scenarios.

Results

We built a formal model for the process of assigning human resources to software project teams. This model takes into account as many factors as possible and aids the assignment of individuals to project roles, as well as the formation of the team as a whole.We found that the rules that were identified to form software development project teams are useful. From the tests we found that model implementation was feasible (all the executions of the implemented problem-solving algorithms output feasible solutions in response times that can be considered as acceptable).

Conclusion

Using the Delphi method we were able to propose software project roles and competences. Psychological tests and data mining tools identified useful rules for forming software project teams. These were used to build a formal model. This model was built into a tool that returns role assignments in acceptable response times. This decision support tool helps managers assign people to roles and to form teams. Using the tool, project leaders can flexibly evaluate different team make-ups, taking into account several factors, as well as different constraints and objectives.