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Valencia La Fe-Health Department, aligned with the Regional Ministry of Health, is pushing an Innovative Chronic Diseases Management Program, by introducing a new, proactive, care model based on current policies, best practices and evidence available aiming to a greater control of the patients while they are at homes. The program has been developed working on the seven courses of action, covering all the components required according to experts. This includes a) identification and stratification of the population, b) care processes design in coordination with all health care resources involved according to case management methodology, c) guidelines and protocols documentation and design of educational programs, d) Service portfolio development and care planning and coordination across care level e) promotion of new technologies, f) continuous assessment and improvement of quality of care g) alignment of resources and incentives in the Health Department. The predictive model developed identifies 3.28% of the total population, related to 72% annual consumption of unplanned weighted resources in our reference population. Chronic management program was evaluated through both a quasi-experimental study including 1900 patients enrolled in the case management program and a randomized clinical trial (RCT) including 495 patients on 3 branches (standard practice, phone supported intervention and technology based intervention). RCT preliminary results regarding health-related quality of life and other outcomes variables will be available during the first quarter of 2014.

Frequency assignment problem (FAP) is a very important issue in the field of telecommunications (especially in GSM-Global System for Mobile-Networks). In this work, we present the Population-Based Incremental Learning (PBIL) algorithm to solve a particular branch of the FAP problem (MS-FAP). MS-FAP (Minimum Span Frequency Assignment Problem) tries to minimize the range of frequencies which is necessary in a certain area to cover the communications which take place there. In this paper it is presented the problem and it is explained the methodology which solve it. We have performed tests with a complete set of experiments using seven well known variations of PBIL and 7 types of MS-FAP problems. At the end, the results are presented and we compare them to conclude which variation of PBIL provides the best solution to the MS-FAP problem.

In this paper we study different versions of the PBIL (population-based incremental learning) algorithm to evaluate and try to improve the results obtained by the standard version of the algorithm when it is used to solve a realistic-sized frequency assignment problem (FAP). PBIL is based on genetic algorithms and competitive learning, being a population evolution model based on probabilistic models. On the other hand, it is important to point out that frequency planning is a very important task for current GSM operators. The FAP problem consists in trying to minimize the number of interferences (or conflicts in the communications) caused when a limited number of frequencies has to be assigned to a quite high number of transceivers (and there are much more transceivers than frequencies). In the work presented here we take as initial point the results obtained with the standard version of PBIL and we perform on the one hand a complete study with six variations of the algorithm (PBIL-negativeLR, PBIL-different, etc.) and on the other hand a hybridization between PBIL and a local search method. Our final goal is to discover which approach can compute the most accurate frequency plans for real-world instances.

Frequency planning (also known as frequency assignment problem -FAP-) is a very important task for current GSM operators. The problem consists in trying to minimize the number of interferences caused when a limited number of frequencies has to be assigned to a quite high number of transceivers. In this work we focus on solving this problem for a realistic-sized, real-world GSM network using a parallelized version of the PBIL (population-based incremental learning) algorithm. Therefore, we have parallelized the PBIL algorithm fixed to the FAP problem using cluster computing. The analysis of the results proves that we have reached a double goal: on the one hand, with the parallelized version of the algorithm, its execution time is reduced down to the optimum values; and on the other hand, we prove that using a distributed island model applied to PBIL, the results for the network-planning are better than the ones obtained with the sequential version.