Decision Support Models for Composing and Navigating through e-Learning Objects

Libraries of learning objects may serve as basis for deriving course offerings that are customized to the needs of different learning communities or even individuals. Several ways of organizing this course composition process are discussed. Course composition needs a clear understanding of the dependencies between the learning objects. Therefore we discuss the metadata for object relationships proposed in different standardization projects and especially those suggested in the Dublin Core Metadata Initiative. Based on these metadata we construct adjacency matrices and graphs. We show how Gozinto-type computations can be used to determine direct and indirect prerequisites for certain learning objects. The metadata may also be used to define integer programming models which can be applied to support the instructor in formulating his specifications for selecting objects or which allow a computer agent to automatically select learning objects. Such decision models could also be helpful for a learner navigating through a library of learning objects. We also sketch a graph-based procedure for manual or automatic sequencing of the learning objects.

E-Health towards ecumenical framework for personalized medicine via Decision Support System

The purpose of the present manuscript is to present the advances performed in medicine using a Personalized Decision Support System (PDSS). The models used in Decision Support Systems (DSS) are examined in combination with Genome Information and Biomarkers to produce personalized result for each individual. The concept of personalize medicine is described in depth and application of PDSS for Cardiovascular Diseases (CVD) and Type-1 Diabetes Mellitus (T1DM) are analyzed. Parameters extracted from genes, biomarkers, nutrition habits, lifestyle and biological measurements feed DSSs, incorporating Artificial Intelligence Modules (AIM), to provide personalized advice, medication and treatment.

Can decision making in general surgery be based on evidence? An empirical study of Cochrane Reviews

BACKGROUND: This empirical study analyzes the current status of Cochrane Reviews (CRs) and their strength of recommendation for evidence-based decision making in the field of general surgery. METHODS: Systematic literature search of the Cochrane Database of Systematic Reviews and the Cochrane Collaboration's homepage to identify available CRs on surgical topics. Quantitative and qualitative characteristics, utilization, and formulated treatment recommendations were evaluated by 2 independent reviewers. Association of review characteristics with treatment recommendation was analyzed using univariate and multivariate logistic regression models. RESULTS: Ninety-three CRs, including 1,403 primary studies and 246,473 patients, were identified. Mean number of included primary studies per CR was 15.1 (standard deviation [SD] 14.5) including 2,650 (SD 3,340) study patients. Two and a half (SD 8.3) nonrandomized trials were included per analyzed CR. Seventy-two (77%) CRs were published or updated in 2005 or later. Explicit treatment recommendations were given in 45 (48%). Presence of a treatment recommendation was associated with the number of included primary studies and the proportion of randomized studies. Utilization of surgical CRs remained low and showed large inter-country differences. The most surgical CRs were accessed in UK, USA, and Australia, followed by several Western and Eastern European countries. CONCLUSION: Only a minority of available CRs address surgical questions and their current usage is low. Instead of unsystematically increasing the number of surgical CRs it would be far more efficient to focus the review process on relevant surgical questions. Prioritization of CRs needs valid methods which should be developed by the scientific surgical community.

Predictors of satisfaction with treatment decision, decision-making preferences, and main treatment goals in patients with advanced cancer

Purpose This study investigated satisfaction with treatment decision (SWTD), decision-making preferences (DMP), and main treatment goals, as well as evaluated factors that predict SWTD, in patients receiving palliative cancer treatment at a Swiss oncology network. Patients and methods Patients receiving a new line of palliative treatment completed a questionnaire 4–6 weeks after the treatment decision. Patient questionnaires were used to collect data on sociodemographics, SWTD (primary outcome measure), main treatment goal, DMP, health locus of control (HLoC), and several quality of life (QoL) domains. Predictors of SWTD (6 = worst; 30 = best) were evaluated by uni- and multivariate regression models. Results Of 480 participating patients in eight hospitals and two private practices, 445 completed all questions regarding the primary outcome measure. Forty-five percent of patients preferred shared, while 44 % preferred doctor-directed, decision-making. Median duration of consultation was 30 (range: 10–200) minutes. Overall, 73 % of patients reported high SWTD (≥24 points). In the univariate analyses, global and physical QoL, performance status, treatment goal, HLoC, prognosis, and duration of consultation were significant predictors of SWTD. In the multivariate analysis, the only significant predictor of SWTD was duration of consultation (p = 0.01). Most patients indicated hope for improvement (46 %), followed by hope for longer life (26 %) and better quality of life (23 %), as their main treatment goal. Conclusion Our results indicate that high SWTD can be achieved in most patients with a 30-min consultation. Determining the patient’s main treatment goal and DMP adds important information that should be considered before discussing a new line of palliative treatment.

Structural and Institutional Determinants of Influence Reputation: A Comparison of Collaborative and Adversarial Policy Networks in Decision Making and Implementation

The central assumption in the literature on collaborative networks and policy networks is that political outcomes are affected by a variety of state and nonstate actors. Some of these actors are more powerful than others and can therefore have a considerable effect on decision making. In this article, we seek to provide a structural and institutional explanation for these power differentials in policy networks and support the explanation with empirical evidence. We use a dyadic measure of influence reputation as a proxy for power, and posit that influence reputation over the political outcome is related to vertical integration into the political system by means of formal decision-making authority, and to horizontal integration by means of being well embedded into the policy network. Hence, we argue that actors are perceived as influential because of two complementary factors: (a) their institutional roles and (b) their structural positions in the policy network. Based on temporal and cross-sectional exponential random graph models, we compare five cases about climate, telecommunications, flood prevention, and toxic chemicals politics in Switzerland and Germany. The five networks cover national and local networks at different stages of the policy cycle. The results confirm that institutional and structural drivers seem to have a crucial impact on how an actor is perceived in decision making and implementation and, therefore, their ability to significantly shape outputs and service delivery.

The relation of secondary student's career choice readiness to a six-phase model of career decision-making

Based on common aspects of recent models of career decision-making (CDM) a sixphase model of CDM for secondary students is presented and empirically evaluated. The study tested the hypothesis that students who are in later phases possess more career choice readiness and consider different numbers of career alternatives. 266 Swiss secondary students completed measures tapping phase of CDM, career choice readiness, and number of considered career options. Career choice readiness showed an increase with phase of CDM. Later phases were generally associated with a larger increase in career choice readiness. Number of considered career options showed a curve-linear development with fewer options considered at the beginning and at the end of the process. Male students showed a larger variability in their distribution among the process with more male than female students in the first and last phase of the process. Implications for theory and practice are presented.

Global Optimization with Sparse and Local Gaussian Process Models

We present a novel surrogate model-based global optimization framework allowing a large number of function evaluations. The method, called SpLEGO, is based on a multi-scale expected improvement (EI) framework relying on both sparse and local Gaussian process (GP) models. First, a bi-objective approach relying on a global sparse GP model is used to determine potential next sampling regions. Local GP models are then constructed within each selected region. The method subsequently employs the standard expected improvement criterion to deal with the exploration-exploitation trade-off within selected local models, leading to a decision on where to perform the next function evaluation(s). The potential of our approach is demonstrated using the so-called Sparse Pseudo-input GP as a global model. The algorithm is tested on four benchmark problems, whose number of starting points ranges from 102 to 104. Our results show that SpLEGO is effective and capable of solving problems with large number of starting points, and it even provides significant advantages when compared with state-of-the-art EI algorithms.