333,333 research outputs found
A systematic review with attempted network meta-analysis of asthma therapy recommended for five to eighteen year olds in GINA steps three and four
Background: The recommendations for the treatment of moderate persistent asthma in the Global Initiative for Asthma (GINA) guidelines for paediatric asthma are mainly based on scientific evidence extrapolated from studies in adults or on consensus. Furthermore, clinical decision-making would benefit from formal ranking of treatments in terms of effectiveness.Our objective is to assess all randomized trial-based evidence specifically pertaining to 5-18 year olds with moderate persistent asthma. Rank the different drug treatments of GINA guideline steps 3&4 in terms of effectiveness.Methods: Systematic review with network meta-analysis. After a comprehensive search in Central, Medline, Embase, CINAHL and the WHO search portal two reviewers selected RCTs performed in 4,129 children from 5-18 year old, with moderate persistent asthma comparing any GINA step 3&4 medication options. Further quality was assessed according the Cochrane Collaboration's tool and data-extracted included papers and built a network of the trials. Attempt at ranking treatments with formal statistical methods employing direct and indirect (e.g. through placebo) connections between all treatments.Results: 8,175 references were screened; 23 randomized trials (RCT), comparing head-to-head (n=17) or against placebo (n=10), met the inclusion criteria. Except for theophylline as add-on therapy in step 4, a closed network allowed all comparisons to be made, either directly or indirectly. Huge variation in, and incomplete reporting of, outcome measurements across RCTs precluded assessment of relative efficacies.Conclusion: Evidence-based ranking of effectiveness of drug treatments in GINA steps 3&4 is not possible yet. Existing initiatives for harmonization of outcome measurements in asthma trials need urgent implementation
Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making
In multi-objective decision planning and learning, much attention is paid to
producing optimal solution sets that contain an optimal policy for every
possible user preference profile. We argue that the step that follows, i.e,
determining which policy to execute by maximising the user's intrinsic utility
function over this (possibly infinite) set, is under-studied. This paper aims
to fill this gap. We build on previous work on Gaussian processes and pairwise
comparisons for preference modelling, extend it to the multi-objective decision
support scenario, and propose new ordered preference elicitation strategies
based on ranking and clustering. Our main contribution is an in-depth
evaluation of these strategies using computer and human-based experiments. We
show that our proposed elicitation strategies outperform the currently used
pairwise methods, and found that users prefer ranking most. Our experiments
further show that utilising monotonicity information in GPs by using a linear
prior mean at the start and virtual comparisons to the nadir and ideal points,
increases performance. We demonstrate our decision support framework in a
real-world study on traffic regulation, conducted with the city of Amsterdam.Comment: AAMAS 2018, Source code at
https://github.com/lmzintgraf/gp_pref_elici
A methodology for the selection of new technologies in the aviation industry
The purpose of this report is to present a technology selection methodology to
quantify both tangible and intangible benefits of certain technology
alternatives within a fuzzy environment. Specifically, it describes an
application of the theory of fuzzy sets to hierarchical structural analysis and
economic evaluations for utilisation in the industry. The report proposes a
complete methodology to accurately select new technologies. A computer based
prototype model has been developed to handle the more complex fuzzy
calculations. Decision-makers are only required to express their opinions on
comparative importance of various factors in linguistic terms rather than exact
numerical values. These linguistic variable scales, such as âvery highâ, âhighâ,
âmediumâ, âlowâ and âvery lowâ, are then converted into fuzzy numbers, since it
becomes more meaningful to quantify a subjective measurement into a range rather
than in an exact value. By aggregating the hierarchy, the preferential weight of
each alternative technology is found, which is called fuzzy appropriate index.
The fuzzy appropriate indices of different technologies are then ranked and
preferential ranking orders of technologies are found. From the economic
evaluation perspective, a fuzzy cash flow analysis is employed. This deals
quantitatively with imprecision or uncertainties, as the cash flows are modelled
as triangular fuzzy numbers which represent âthe most likely possible valueâ,
âthe most pessimistic valueâ and âthe most optimistic valueâ. By using this
methodology, the ambiguities involved in the assessment data can be effectively
represented and processed to assure a more convincing and effective decision-
making process when selecting new technologies in which to invest. The prototype
model was validated with a case study within the aviation industry that ensured
it was properly configured to meet the
Hashing as Tie-Aware Learning to Rank
Hashing, or learning binary embeddings of data, is frequently used in nearest
neighbor retrieval. In this paper, we develop learning to rank formulations for
hashing, aimed at directly optimizing ranking-based evaluation metrics such as
Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG). We
first observe that the integer-valued Hamming distance often leads to tied
rankings, and propose to use tie-aware versions of AP and NDCG to evaluate
hashing for retrieval. Then, to optimize tie-aware ranking metrics, we derive
their continuous relaxations, and perform gradient-based optimization with deep
neural networks. Our results establish the new state-of-the-art for image
retrieval by Hamming ranking in common benchmarks.Comment: 15 pages, 3 figures. IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 201
A Methodology for the Selection of Multi-Criteria Decision Analysis Methods in Real Estate and Land Management Processes
Real estate and land management are characterised by a complex, elaborate combination of technical, regulatory and governmental factors. In Europe, Public Administrators must address the complex decision-making problems that need to be resolved, while also acting in consideration of the expectations of the different stakeholders involved in settlement transformation. In complex situations (e.g., with different aspects to be considered and multilevel actors involved), decision-making processes are often used to solve multidisciplinary and multidimensional analyses, which support the choices of those who are making the decision. Multi-Criteria Decision Analysis (MCDA) methods are included among the examination and evaluation techniques considered useful by the European Community. Such analyses and techniques are performed using methods, which aim to reach a synthesis of the various forms of input data needed to define decision-making problems of a similar complexity. Thus, one or more of the conclusions reached allow for informed, well thought-out, strategic decisions. According to the technical literature on MCDA, numerous methods are applicable in different decision-making situations, however, advice for selecting the most appropriate for the specific field of application and problem have not been thoroughly investigated. In land and real estate management, numerous queries regarding evaluations often arise. In brief, the objective of this paper is to outline a procedure with which to select the method best suited to the specific queries of evaluation, which commonly arise while addressing decision-making problems. In particular issues of land and real estate management, representing the so-called âsettlement sectorâ. The procedure will follow a theoretical-methodological approach by formulating a taxonomy of the endogenous and exogenous variables of the multi-criteria analysis method
- âŠ