90,778 research outputs found
A Hybrid Data-Driven Web-Based UI-UX Assessment Model
Today, a large proportion of end user information systems have their
Graphical User Interfaces (GUI) built with web-based technology (JavaScript,
CSS, and HTML). Some of these web-based systems include: Internet of Things
(IOT), Infotainment (in vehicles), Interactive Display Screens (for digital
menu boards, information kiosks, digital signage displays at bus stops or
airports, bank ATMs, etc.), and web applications/services (on smart devices).
As such, web-based UI must be evaluated in order to improve upon its ability to
perform the technical task for which it was designed. This study develops a
framework and a processes for evaluating and improving the quality of web-based
user interface (UI) as well as at a stratified level. The study develops a
comprehensive framework which is a conglomeration of algorithms such as the
multi-criteria decision making method of analytical hierarchy process (AHP) in
coefficient generation, sentiment analysis, K-means clustering algorithms and
explainable AI (XAI)
Stratification Trees for Adaptive Randomization in Randomized Controlled Trials
This paper proposes an adaptive randomization procedure for two-stage
randomized controlled trials. The method uses data from a first-wave experiment
in order to determine how to stratify in a second wave of the experiment, where
the objective is to minimize the variance of an estimator for the average
treatment effect (ATE). We consider selection from a class of stratified
randomization procedures which we call stratification trees: these are
procedures whose strata can be represented as decision trees, with differing
treatment assignment probabilities across strata. By using the first wave to
estimate a stratification tree, we simultaneously select which covariates to
use for stratification, how to stratify over these covariates, as well as the
assignment probabilities within these strata. Our main result shows that using
this randomization procedure with an appropriate estimator results in an
asymptotic variance which is minimal in the class of stratification trees.
Moreover, the results we present are able to accommodate a large class of
assignment mechanisms within strata, including stratified block randomization.
In a simulation study, we find that our method, paired with an appropriate
cross-validation procedure ,can improve on ad-hoc choices of stratification. We
conclude by applying our method to the study in Karlan and Wood (2017), where
we estimate stratification trees using the first wave of their experiment
Integrating patients' views into health technology assessment: Analytic hierarchy process (AHP) as a method to elicit patient preferences
Background: Patient involvement is widely acknowledged to be a valuable component in health technology assessment (HTA) and healthcare decision making. However, quantitative approaches to ascertain patients' preferences for treatment endpoints are not yet established. The objective of this study is to introduce the analytic hierarchy process (AHP) as a preference elicitation method in HTA. Based on a systematic literature review on the use of AHP in health care in 2009, the German Institute for Quality and Efficiency in Health Care (IQWiG) initiated an AHP study related to its HTA work in 2010. - \ud
Methods: The AHP study included two AHP workshops, one with twelve patients and one with seven healthcare professionals. In these workshops, both patients and professionals rated their preferences with respect to the importance of different endpoints of antidepressant treatment by a pairwise comparison of individual endpoints. These comparisons were performed and evaluated by the AHP method and relative weights were generated for each endpoint. - \ud
Results: The AHP study indicates that AHP is a well-structured technique whose cognitive demands were well handled by patients and professionals. The two groups rated some of the included endpoints of antidepressant treatment differently. For both groups, however, the same six of the eleven endpoints analyzed accounted for more than 80 percent of the total weight. - \ud
Conclusions: AHP can be used in HTA to give a quantitative dimension to patients' preferences for treatment endpoints. Preference elicitation could provide important information at various stages of HTA and challenge opinions on the importance of endpoints
Estimating Marginal Hazard Ratios by Simultaneously Using A Set of Propensity Score Models: A Multiply Robust Approach
The inverse probability weighted Cox model is frequently used to estimate marginal hazard ratios. Its validity requires a crucial condition that the propensity score model is correctly specified. To provide protection against misspecification of the propensity score model, we propose a weighted estimation method rooted in empirical likelihood theory. The proposed estimator is multiply robust in that it is guaranteed to be consistent when a set of postulated propensity score models contains a correctly specified model. Our simulation studies demonstrate satisfactory finite sample performance of the proposed method in terms of consistency and efficiency. We apply the proposed method to compare the risk of postoperative hospitalization between sleeve gastrectomy and Roux-en-Y gastric bypass using data from a large medical claims and billing database.We further extend the development to multi-site studies to enable each site to postulate multiple site-specific propensity score models
The selection of case studies: strategies and their applications to IS implementation case studies
Case study research by definition is well suited to the study of IS implementation, especially when
context is important. Furthermore, its products are highly relevant and therefore they appeal to IS
practitioners, an audience for which the IS literature has been critiqued of ignoring. While the value of
single case research is methodologically viable in the study of critical cases, the multiple case study
approach is believed to be more appropriate to the study of typical cases of IS implementations. However,
the IS literature provides little guidance on strategies for case study selection, particularly for multiple
case studies. More important, is the need to provide the rational for case selection that relates these
suggested strategies to the particular objectives of the case research inquiry. The purpose of this study is
to fill this gap by providing a review of strategies for single and multiple case study selection in the
context of systems implementation. Furthermore, the application of these guidelines in a multiple case
study of strategic decision making of enterprise systems implementations will be illustrated
Towards more inclusive long-term bulk water resource management
Fresh water resources provide a platform for complex and often emotional issues to develop, particularly in resource scarcity situations. Bulk water infrastructure contains elements of a public good and proved vulnerable to failures in market and government driven allocation strategies. Common to both are uncaptured costs and benefits due to shortcomings in cost quantification techniques. Natural ecosystems stands to lose the most since ecosystem services are often not quantifiable in monetary terms and therefore neglected in allocation decision-making. This paper took on the challenge of expanding current decision-support in order to promote more inclusive long-term water management. A case-study approach with the focus on a choice related problem regarding different long-term bulk water resource management options was applied in the Western Cape province. The paper incorporated components of economic valuation theory, a public survey and a modified Delphi expert panel technique. Both spatial and temporal dimensions of the decision-making context were expanded. Two surveys were completed to accommodate these expansions. The first focused on public preference in water allocation management and the relative merit of accommodating public preference in highly specialised decision-making such as long-term water allocation decision-making. The second survey utilized a modified Delphi technique in which an expert panel indicated the relative merit of two alternative long-term allocation strategies. A willingness to pay for 'greener' water was observed and may be used to motivate a paradigm shift from management's perspective to consider, without fear of harming their own political position, 'greener' water supply options more seriously even if these options imply higher direct costs to public.water management, decision-support, public participation, Resource /Energy Economics and Policy,
Recommended from our members
Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings.
We applied machine learning algorithms for differentiation of posterior fossa tumors using apparent diffusion coefficient (ADC) histogram analysis and structural MRI findings. A total of 256 patients with intra-axial posterior fossa tumors were identified, of whom 248 were included in machine learning analysis, with at least 6 representative subjects per each tumor pathology. The ADC histograms of solid components of tumors, structural MRI findings, and patients' age were applied to construct decision models using Classification and Regression Tree analysis. We also compared different machine learning classification algorithms (i.e., naĂŻve Bayes, random forest, neural networks, support vector machine with linear and polynomial kernel) for dichotomized differentiation of the 5 most common tumors in our cohort: metastasis (n = 65), hemangioblastoma (n = 44), pilocytic astrocytoma (n = 43), ependymoma (n = 27), and medulloblastoma (n = 26). The decision tree model could differentiate seven tumor histopathologies with terminal nodes yielding up to 90% accurate classification rates. In receiver operating characteristics (ROC) analysis, the decision tree model achieved greater area under the curve (AUC) for differentiation of pilocytic astrocytoma (p = 0.020); and atypical teratoid/rhabdoid tumor ATRT (p = 0.001) from other types of neoplasms compared to the official clinical report. However, neuroradiologists' interpretations had greater accuracy in differentiating metastases (p = 0.001). Among different machine learning algorithms, random forest models yielded the highest accuracy in dichotomized classification of the 5 most common tumor types; and in multiclass differentiation of all tumor types random forest yielded an averaged AUC of 0.961 in training datasets, and 0.873 in validation samples. Our study demonstrates the potential application of machine learning algorithms and decision trees for accurate differentiation of brain tumors based on pretreatment MRI. Using easy to apply and understandable imaging metrics, the proposed decision tree model can help radiologists with differentiation of posterior fossa tumors, especially in tumors with similar qualitative imaging characteristics. In particular, our decision tree model provided more accurate differentiation of pilocytic astrocytomas from ATRT than by neuroradiologists in clinical reads
Benchmark of machine learning methods for classification of a Sentinel-2 image
Thanks to mainly ESA and USGS, a large bulk of free images of the Earth is readily available nowadays. One of the main goals of
remote sensing is to label images according to a set of semantic categories, i.e. image classification. This is a very challenging issue
since land cover of a specific class may present a large spatial and spectral variability and objects may appear at different scales and
orientations.
In this study, we report the results of benchmarking 9 machine learning algorithms tested for accuracy and speed in training and
classification of land-cover classes in a Sentinel-2 dataset. The following machine learning methods (MLM) have been tested: linear
discriminant analysis, k-nearest neighbour, random forests, support vector machines, multi layered perceptron, multi layered
perceptron ensemble, ctree, boosting, logarithmic regression. The validation is carried out using a control dataset which consists of an
independent classification in 11 land-cover classes of an area about 60 km2, obtained by manual visual interpretation of high resolution
images (20 cm ground sampling distance) by experts. In this study five out of the eleven classes are used since the others have too few
samples (pixels) for testing and validating subsets. The classes used are the following: (i) urban (ii) sowable areas (iii) water (iv) tree
plantations (v) grasslands.
Validation is carried out using three different approaches: (i) using pixels from the training dataset (train), (ii) using pixels from the
training dataset and applying cross-validation with the k-fold method (kfold) and (iii) using all pixels from the control dataset. Five
accuracy indices are calculated for the comparison between the values predicted with each model and control values over three sets of
data: the training dataset (train), the whole control dataset (full) and with k-fold cross-validation (kfold) with ten folds. Results from
validation of predictions of the whole dataset (full) show the random forests method with the highest values; kappa index ranging from
0.55 to 0.42 respectively with the most and least number pixels for training. The two neural networks (multi layered perceptron and its
ensemble) and the support vector machines - with default radial basis function kernel - methods follow closely with comparable
performanc
Analysis of industry 4.0 implementation in mobility sector: An integrated approach based on QFD, BWM, and stratified combined compromise solution under fuzzy environment
The role of new technologies in industrial and service sector is inevitable. Various sectors like transport / mobility have decided to remodel and redesign their infrastructures by implementing innovative devices and strategies. Transport / mobility sector is one of the most fast-growing industries which demands innovative solutions, however, it will be complex to derive optimal decision while one confront uncertain conditions and variables. In this paper, we develop a decision support system for technology adoption in transport / mobility division within the context of Industry 4.0 considering a case study in Spain. To find the adopted technology in this sector, several alternatives (options) and variables (criteria) should be assumed. We propose an integrated decision-making system including quality function deployment (QFD) and best-worst method (BWM) to find the importance weight of each criterion. After we apply the stratified Combined compromise solution (S-CoCoSo) to rate the alternatives and rank them under a multi-scenario perspective. The results will be analyzed through some sensitivity analysis actions. The novelty of our proposed decision support model contributes to the mobility sector and releases guidelines to managers and policy makers
- …