Abstract
Electronic access to patient's medication history (PMH) in the emergency department (ED) in Portugal is not widely granted, nor has the importance of such access been clearly assessed. Given the known association between poor PMH and medication errors, the goal of this study was to gather requirements for such a system, assessing physicians' opinions regarding the importance of having access to PMH in the ED. A questionnaire was sent to all Portuguese public hospitals which approved the study, and forwarded by email by the internal services of each hospital to ED physicians. Fourteen hospitals authorized the study, from which 83 ED physicians answered the questionnaire. PMH-related information considered most important focused on medication name and posology (> 90%) and date and dose of prescription (> 80%), but also date of dispensing of medications (> 40%). Other information such as allergies (99%) and adverse reactions (96%) were similarly considered important, and physicians agree with the inclusion of nonprescription medications (85%) as well as homeopathic medicines (64%). Overall, access to PMH in the ED appears to be important and present benefits to patients' care. Given this, electronic access to PHM should be settled in Portuguese ED.

Abstract
Obstructive Sleep Apnea (OSA) affects 2-4% of the population worldwide. The standard test for OSA diagnosis is polysomnography (PSG), an expensive exam limited to urban areas. Furthermore, nearly half of all PSG tests results are negative for OSA. This work aims to reduce these unnecessary exams, by defining an auxiliary diagnostic method that could be used to assess patient's need for PSG, according to their probability of OSA diagnosis. A prospective study was conducted on adult patients with OSA suspicion who performed PSG at our sleep laboratory in Portugal. The studied clinical variables were defined after literature review and collected during consultation. Two comparable cohorts were studied for derivation (n=86) and validation (n=33) of models. Three classifiers were analyzed - a multiple logistic regression classifier (AUC=80.0%) and two Bayesian networks classifiers - Naive Bayes (AUC=81.3%) and Tree Augmented Naive Bayes (TAN, AUC=81.4%) - aiming at the best possible specificity (identification of unnecessary exams). Overall, sensitivity-adjusted models could detect normal patients, preventing unnecessary PSG, while keeping sensitivity high. Furthermore, the graphical representation of TAN can be explored by the physician during consultation, making it a helpful tool to assess patients' need to perform PSG.

Abstract
The vast majority of studies in meta-learning uses only few performance measures when characterizing different machine learning algorithms. The measure Adjusted Ratios of Ratio (ARR) addresses the problem of how to evaluate the quality of a model based on the accuracy and training time. Unfortunately, this measure suffers from a shortcoming that is described in this paper. A new solution is proposed and it is shown that the proposed function satisfies the criterion of monotonicity, unlike ARR.

Abstract
Background: Transmission models can aid understanding of disease dynamics and are useful in testing the efficiency of control measures. The aim of this study was to formulate an appropriate stochastic Susceptible-Infectious-Resistant/ Carrier (SIR) model for Salmonella Typhimurium in pigs and thus estimate the transmission parameters between states. Results: The transmission parameters were estimated using data from a longitudinal study of three Danish farrow-to-finish pig herds known to be infected. A Bayesian model framework was proposed, which comprised Binomial components for the transition from susceptible to infectious and from infectious to carrier; and a Poisson component for carrier to infectious. Cohort random effects were incorporated into these models to allow for unobserved cohort-specific variables as well as unobserved sources of transmission, thus enabling a more realistic estimation of the transmission parameters. In the case of the transition from susceptible to infectious, the cohort random effects were also time varying. The number of infectious pigs not detected by the parallel testing was treated as unknown, and the probability of non-detection was estimated using information about the sensitivity and specificity of the bacteriological and serological tests. The estimate of the transmission rate from susceptible to infectious was 0.33 [0.06, 1.52], from infectious to carrier was 0.18 [0.14, 0.23] and from carrier to infectious was 0.01 [0.0001, 0.04]. The estimate for the basic reproduction ration (R-0) was 1.91 [0.78, 5.24]. The probability of non-detection was estimated to be 0.18 [0.12, 0.25]. Conclusions: The proposed framework for stochastic SIR models was successfully implemented to estimate transmission rate parameters for Salmonella Typhimurium in swine field data. R0 was 1.91, implying that there was dissemination of the infection within pigs of the same cohort. There was significant temporal-cohort variability, especially at the susceptible to infectious stage. The model adequately fitted the data, allowing for both observed and unobserved sources of uncertainty (cohort effects, diagnostic test sensitivity), so leading to more reliable estimates of transmission parameters.

Abstract
In traffic & transportation system analysis the way individuals make choices plays a paramount role as these will affect the general efficiency with which people can travel. Modifications on the system by means of policy intervention affect commuters' perspective impacting on the performance of the network and eventually on the society's welfare. The emergence of system's behaviour, as a result of decisions at individual level, provides the traffic manager with the opportunity to evaluate modifications that have been implemented on the system. However, there has been a slow advance in appropriately representing users and their behaviour in all social dimensions of intelligent transportation systems. This paper discusses on a social-oriented modelling & simulation framework for Artificial Transportation Systems, which accounts for different social dimensions of the system in the assessment and application of policy procedures. We illustrate how a social agent-based model can be a useful tool to test the appropriateness and efficiency of transportation policies.

Abstract
Mobility and commuting in metropolitan areas are very expensive, highly polluted and time wasting. The Four Step Model (FSM) is the key model to analyze a Transportation Network. However, being the FSM a combination of several models, combining them in one model have rarely been applied. To deal with this problem an Agent-Based Model (ABM) is proposed. An ABM uses the metaphor of autonomous agents and so, they can be a handful tool for combining different models in one. Therefore, this model can be used as a tool for simulation and integrate the FSM in one model. Here we present the preliminary results of this approach. (C) 2014 The Authors. Published by Elsevier B.V.