Changes In Values Of Variables Research Articles

Application of a decision support system for providing better mental health care: the case of the Basque Country (Spain)

IntroductionDecision Support Systems (DSS) are appropriate tools for guiding policymaking processes in Mental Health (MH) management, especially where a balanced and integrated care provision is required.ObjectivesTo assess the performance of a MH ecosystem for identifying benchmark and target-for-improvement catchment areas according to the Balanced Care model.Methods The MH provision, distinguishing inpatient, day and outpatient main types of care, has been assessed in the Mental Health Network of Gipuzkoa (Basque Country, Spain) using a DSS, integrating Data Envelopment Analysis, Monte-Carlo Simulation and Artificial Intelligence. 13 catchment areas, defined by a reference MH centre, are the units (universe) for the analysis. The indicators for MH ecosystem performance were: relative technical efficiency, stability and entropy, for identifying both benchmarking and target-for-improvement areas. The analysis of the differences between the two groups can be used to design organizational interventions.ResultsThe Mental Health Network of Gipuzkoa showed high global efficiency scores, but it can be considered statistically unstable (small changes in variable values can have relevant impacts on its performance). For a global performance improvement, it is recommended to reduce admissions and readmissions in inpatient care, increase workforce capacity and utilization of day care services and, finally, increase the availability of outpatient care services.ConclusionsThis research offers a guide for evidence-informed policy-making to improve MH care provision in the main types of care and provide aftercare. The characteristics of the area to be improved are critical to design interventions and assess their potential impact on the MH ecosystem.DisclosureNo significant relationships.

107. A Computational Model of Adenovirus Infection

Computational modeling of biological processes provides two distinct benefits for empirical investigators. First, computational models test the impact of an immense array of variables at a broad range of values rapidly and inexpensively in silico. Second, a computational model teaches scientists about the magnitude of an impact due to changes in values of variables. The goal of this project is to develop a highly flexible, user-friendly, biologically relevant model of DNA virus infection. Adenovirus is used as a comparator for the purpose of establishing states (distinct environments) and for identifying the processes that permit transfer from state to state, the rates of those transitions, and the three-dimensional position of each virus at each time point. States in this model include free extracellular virus, cell-surface bound virus, endosome virus, lysosome virus, cytosolic virus, microtubule-associated virus, microtubule-organizing center-associated virus, nuclear bound virus, and inactivated virus. Rates governing transitions between states include diffusion rates, binding and dissociation rates, and microtubule-associated motility, among others. The model employs a game controller to introduce stochastic variability around the chosen rate ensuring that no two iterations of the model give the same dataset. Outputs from the model include comma-separated-value (.csv) data files that are amenable to spreadsheet analysis, graphs showing the relative population of viruses in each state (hundreds of viruses can be used in each infection), and three-dimensional images of the infected cell. An initial characterization of the model demonstrates the veracity of several individual elements. For example, with a pre-set half time for endocytic internalization of bound virus from the cell surface, one would predict that 87.5% of bound virus at time zero would enter the cell in 3 half times (7.5 minutes). Three iterations of the model predicted internalization of 84.2 +/- 13.9% (mean +/- sd) of bound virus over that time period. Similarly, modeling diffusion of vectors in the cytoplasm is an important element in understanding the contribution of microtubule-associated motility to DNA delivery to the nucleus. A prior study of adenovirus diffusion in aqueous medium indicated a diffusion coefficient of 3.67 +/- 0.03 × 10^-8 cm2sec-1 (Oliver et al., 1976, BBA 437:589). The cytoplasm is known to impede diffusion, especially for particles like adenovirus that are larger than 50 nm (Seksek et al., 1997, JCB 138:131), leading to a reduction of the diffusion coefficient by at least two orders of magnitude. Using conditions that allow for a total linear translocation of 1000 nm per sec with a stochastic re-setting of direction 200 times per sec, three iterations of the model predict a diffusion coefficient of 1.80 +/-0.48 × 10^-11 cm2sec-1 (mean +/- se), 204x less than diffusion of adenovirus in water. The model will be validated against published experimental data and will incorporate point-and-click selection of infection conditions. In summary, the model provides a user-friendly, visual and quantitative tool for performing computational studies of viral infection.

Construction, dimension reduction and uncertainty analysis of an index of trust in public institutions

Research on social capital in general and trust in particular is markedly increased. The worldwide problem of low and decreasing levels of trust in many public institutions is greatly debated. This is a very important problem because the trust citizens have in public institutions may foster democratic practices and facilitate better provision of public services. Moreover, trust is a central element of social order and survival of any democratic regime: it affects institutional performance, wellbeing, economic development and crime reduction. Public institutions should pay very much attention to citizens’ trust in them and in particular when introducing reforms aiming at improving their functioning. Trust is difficult to be assessed due to its quite elusive meaning. An index of general trust in public institutions is proposed within the framework of composite indicators. It is shown how to reduce the dimension of the index by finding out the most and least important aspects of it. Uncertainty analysis is used to test whether dimension reduction results are robust or not with respect to plausible changes in variable values and variable transformations. An application to data from the European Social Survey is discussed. The results show the very important role of the trust in politicians and political parties in explaining the level of general trust in public institutions.

Incorporating dynamics for predicting poor outcome in acute liver failure patients

Acute liver failure (ALF), also known as fulminant hepatic failure (FHF), is a devastating clinical syndrome with a high mortality of 60%-90%. An early and exact assessment of the severity of ALF together with prediction of its further development is critical in order to determine the further management of the patient. A number of prognostic models have been used for outcome prediction in ALF patients but they are mostly based on the variables measured at one time point, mostly at admission. ALF patients rarely show a static state: rapid progress to a life threatening situation occurs in many patients. Since ALF is a dynamic process, admission values of prognostic variables change over time during the clinical course of the patient. Kumar et al developed a prognostic model [ALF early dynamic (ALFED)] based on early changes in values of variables which predicted outcome. ALFED is a model which seems to be worthwhile to test in ALF patients in other parts of the world with different aetiologies. Since the exact pathophysiology of ALF is not fully known and is certainly complex, we believe that adding promising variables involved in the pathophysiology of ALF to the dynamic approach might even further improve prognostic performance. We agree with Kumar et al that an improved dynamic prognostic model should be based on simplicity (easily to be performed at the bedside) and accuracy. Our comments presented in this paper may be considered as recommendations for future optimization of ALF prediction models.

Serum cortisol concentration and force plate analysis in the assessment of pain associated with sodium urate–induced acute synovitis in dogs

To determine the relationship between serum cortisol concentration and pain severity as measured by force platform gait analysis in dogs with experimentally induced synovitis of the stifle joint. 10 healthy hound-type dogs. Dogs underwent 2 study phases. In the first phase, serum cortisol concentration, systolic arterial blood pressure, heart rate, and gait data were obtained at 0 (first sample), 2.5, 5, 7.5, and 10 hours. In the second phase, the same data were gathered immediately before (0 hours) and 2.5, 5, 7.5, and 10 hours after induction of acute urate synovitis in the left stifle joint. Data were statistically evaluated to compare changes in variable values over time and to determine the accuracy of serum cortisol measurements for diagnosis of acute orthopedic pain. Following induction of synovitis, ground reaction forces were significantly decreased relative to preinduction values at 2.5, 5.0, 7.5, and 10.0 hours and serum cortisol concentration was significantly increased at 2.5 hours. A cortisol concentration of >or= 1.6 microg/dL indicated pain with a 91% sensitivity and 35% specificity. In this model, cortisol concentration may be useful for diagnosing pain in dogs. Although, with a cutoff of >or= 1.6 microg/dL, pain would be detected in most dogs with pain, some pain-free dogs would also be identified as having pain. Conversely, dogs with a serum cortisol of < 1.6 microg/dL would be unlikely to have pain. Validation of this diagnostic test in a large, heterogeneous group of clinical patients is necessary.

Continuity analysis of programs

We present an analysis to automatically determine if a program represents a continuous function, or equivalently, if infinitesimal changes to its inputs can only cause infinitesimal changes to its outputs. The analysis can be used to verify the robustness of programs whose inputs can have small amounts of error and uncertainty---e.g., embedded controllers processing slightly unreliable sensor data, or handheld devices using slightly stale satellite data. Continuity is a fundamental notion in mathematics. However, it is difficult to apply continuity proofs from real analysis to functions that are coded as imperative programs, especially when they use diverse data types and features such as assignments, branches, and loops. We associate data types with metric spaces as opposed to just sets of values, and continuity of typed programs is phrased in terms of these spaces. Our analysis reduces questions about continuity to verification conditions that do not refer to infinitesimal changes and can be discharged using off-the-shelf SMT solvers. Challenges arise in proving continuity of programs with branches and loops, as a small perturbation in the value of a variable often leads to divergent control-flow that can lead to large changes in values of variables. Our proof rules identify appropriate ``synchronization points'' between executions and their perturbed counterparts, and establish that values of certain variables converge back to the original results in spite of temporary divergence. We prove our analysis sound with respect to the traditional epsilon-delta definition of continuity. We demonstrate the precision of our analysis by applying it to a range of classic algorithms, including algorithms for array sorting, shortest paths in graphs, minimum spanning trees, and combinatorial optimization. A prototype implementation based on the Z3 SMT-solver is also presented.

Multivariate Temporal Data Analysis Using Self-Organizing Maps. 1. Training Methodology for Effective Visualization of Multistate Operations

Multistate operations are common in chemical plants and result in high-dimensional, multivariate, temporal data. In this two-part paper, we develop self-organizing map (SOM)-based approaches for visualizing and analyzing such data. In Part 1 of this paper, the SOM is used to reduce the dimensionality of the data and effectively visualize multistate operations in a three-dimensional map. A key characteristic of multistate processes is that the plant operates for long durations at steady states and undergoes brief transitions involving large changes in variable values. When classical SOM training algorithms are used on data from multistate processes, large portions of the SOM become dedicated to steady states, which exaggerates even minor noise in the data. Also, transitions are represented as discrete jumps on the SOM space, which makes it an ineffective tool for visualizing multistate operations. In this Part 1, we propose a new training strategy specifically targeted at multistate operations. In the proposed strategy, the training dataset is first resampled to yield equal representation of the different process states. The SOM is trained with this state-sampled dataset. Furthermore, clustering is applied to group neurons of high similarity into compact clusters. Through this strategy, modes and transitions of multistate operations are depicted differently, with process modes visualized as intuitive clusters and transitions as trajectories across the SOM. We illustrate the proposed strategy using two real-case studies, namely, startup of a laboratory-scale distillation unit and operation of a refinery hydrocracker.

Prognostic indicators in a Danish hospital-based population of colic horses.

A prospective survey of 528 colic horses, referred to the Large Animal Hospital at the Royal Veterinary and Agricultural University of Copenhagen, Denmark, during the period August 1994 to December 1998, was undertaken to develop a predictive model for application in the clinical assessment of prognosis. In the multivariate logistic regression analysis, 357 colic cases were used in the elaboration of a simple clinical-practical model consisting of degree of pain, packed cell volume, capillary refill time and rectal temperature. The relationship between rectal temperature and outcome (survival/death) has been regarded as linear. It has also been reported to be nonsignificant. The present study suggests a strong U-shaped relation, which is easily transformed into a linear association and readily interpretable in the clinical situation if treated as a deviation from 38 degrees C. Several other clinical and laboratory variables were strongly related to outcome in the bivariate analysis. The changes in sensitivity and specificity of the multivariate model, when applied as a prognostic test, were presented with changing cut-off values. The cut-off value is the level, of predicted probability of death, at which the clinical decision to treat or subject to euthanasia is taken. The predictive performance of the model was further illustrated using a mortality of 19%. The optimal accurate classification for both survival and death was 87%, which was attained when a cut-off value of 86% was selected. To minimise the number of horses unnecessarily subjected to euthanasia, the cut-off value was increased. However, this simultaneously increased the number of misclassified survivors, i.e. the number of horses that would die despite treatment. Outcome was especially poorly predicted in 4 horses, as was indicated by extreme deviance residuals. In 2 of these horses the large residuals were attributable to sudden and severe changes in the course of disease. All 4 horses had changes in variable values towards abnormality prior to death. Repeated measurements are therefore suggested in order to increase the test performance in general. The prerequisites of clinical application of prognostic models are critically discussed. The main findings of the present study indicate that degree of pain, packed cell volume, capillary refill time and temperature deviation from 38 degrees C, used in a logistic regression model, offer a strong model for clinical assessment of prognosis.

Probabilistic coastline position computations

The classical deterministic approach of the simulation of coastline development often gives unreliable results due to the uncertainty in the values of the coastal variables; in the input data of simulation models, and in the models themselves. With the probabilistic approach of the simulation, using probability distributions of the coastal variables and the model factors, it is possible to forecast coastline development with a chosen degree of certainty. In a design context one can calculate the failure probability of the planned coastal defence systems. Probabilistic methods have the additional advantage that the insight in the influence of coastal processes and of changes in variable values on intermediate coastline development will be enhanced. It is stressed that the existence of correlation between coastal variables influences the results considerably.