Data Preparation for Logistic Modelling of Flood Crisis in GIS Environment

2014 ◽  
Vol 708 ◽  
pp. 271-275
Author(s):  
Monika Blistanova ◽  
Peter Blistan
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Natural Disasters ◽  
Economic Loss ◽  
Data Preparation ◽  
Civilian Population ◽  
Rescue Work ◽  
Wide Range ◽  
Gis Environment ◽  
Logistic Modelling

Floods are among the most frequent and costly natural disasters in terms of human and economic loss. Flood salvage operations concentrate on rescuing the civilian population, which must take into account many variables. In view of practice difficult planning related with the rescue work is also important logistical support. GIS systems offer a wide range of tools for data analysis and preparation of various scenarios and thus significantly helping in planning and decision-making.

Medical Diagnosis from a Clinician’s Point of View

1978 ◽  
Vol 17 (01) ◽  
pp. 28-35
Author(s):  
F. T. De Dombal
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Computer Science ◽  
Medical Diagnosis ◽  
Information Science ◽  
Point Of View ◽  
Joint Research ◽  
The World

This paper discusses medical diagnosis from the clinicians point of view. The aim of the paper is to identify areas where computer science and information science may be of help to the practising clinician. Collection of data, analysis, and decision-making are discussed in turn. Finally, some specific recommendations are made for further joint research on the basis of experience around the world to date.

Integrative Data Analysis from a Unifying Research Synthesis Perspective

2018 ◽  
Author(s):  
Eun-Young Mun ◽  
Anne E. Ray
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Research Synthesis ◽  
Alcohol Intervention ◽  
Data Set ◽  
Integrative Data Analysis ◽  
Level Data ◽  
Model Complex ◽  
Wide Range ◽  
Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

Accuracy and sensitivity of different Bayesian methods for genomic prediction using simulation and real data

2020 ◽  
Vol 19 (3) ◽  
Author(s):  
Saheb Foroutaifar
Keyword(s):  
Data Analysis ◽  
Bayesian Methods ◽  
Milk Fat ◽  
Prediction Accuracy ◽  
Real Data ◽  
Fat Percentage ◽  
Real Data Analysis ◽  
Wide Range ◽  
High Heritability ◽  
Qtl Effects

AbstractThe main objectives of this study were to compare the prediction accuracy of different Bayesian methods for traits with a wide range of genetic architecture using simulation and real data and to assess the sensitivity of these methods to the violation of their assumptions. For the simulation study, different scenarios were implemented based on two traits with low or high heritability and different numbers of QTL and the distribution of their effects. For real data analysis, a German Holstein dataset for milk fat percentage, milk yield, and somatic cell score was used. The simulation results showed that, with the exception of the Bayes R, the other methods were sensitive to changes in the number of QTLs and distribution of QTL effects. Having a distribution of QTL effects, similar to what different Bayesian methods assume for estimating marker effects, did not improve their prediction accuracy. The Bayes B method gave higher or equal accuracy rather than the rest. The real data analysis showed that similar to scenarios with a large number of QTLs in the simulation, there was no difference between the accuracies of the different methods for any of the traits.

scholarly journals A survey of methods for evaluating mini-publics

2021 ◽  
Author(s):  
Takeuchi Ayano
Keyword(s):  
Decision Making ◽  
Academic Research ◽  
Future Research ◽  
The Political ◽  
Decision Making Process ◽  
Sufficient Information ◽  
Practical Case ◽  
Political Decision ◽  
Wide Range ◽  
Political Decision Making

AbstractPublic participation has become increasingly necessary to connect a wide range of knowledge and various values to agenda setting, decision-making and policymaking. In this context, deliberative democratic concepts, especially “mini-publics,” are gaining attention. Generally, mini-publics are conducted with randomly selected lay citizens who provide sufficient information to deliberate on issues and form final recommendations. Evaluations are conducted by practitioner researchers and independent researchers, but the results are not standardized. In this study, a systematic review of existing research regarding practices and outcomes of mini-publics was conducted. To analyze 29 papers, the evaluation methodologies were divided into 4 categories of a matrix between the evaluator and evaluated data. The evaluated cases mainly focused on the following two points: (1) how to maintain deliberation quality, and (2) the feasibility of mini-publics. To create a new path to the political decision-making process through mini-publics, it must be demonstrated that mini-publics can contribute to the decision-making process and good-quality deliberations are of concern to policy-makers and experts. Mini-publics are feasible if they can contribute to the political decision-making process and practitioners can evaluate and understand the advantages of mini-publics for each case. For future research, it is important to combine practical case studies and academic research, because few studies have been evaluated by independent researchers.

scholarly journals Results of the Behavioral Health Readiness Evaluation and Decision-Making Instrument Study

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 142-152
Author(s):  
Justin M Curley ◽  
Katie L Nugent ◽  
Kristina M Clarke-Walper ◽  
Elizabeth A Penix ◽  
James B Macdonald ◽  
...  
Keyword(s):  
Decision Making ◽  
Behavioral Health ◽  
User Satisfaction ◽  
Safety Issue ◽  
Walter Reed Army Institute ◽  
End User ◽  
Chi Square ◽  
Proficiency Level ◽  
Wide Range ◽  
Decision Making Support

ABSTRACT Introduction Recent reports have demonstrated behavioral health (BH) system and individual provider challenges to BH readiness success. These pose a risk to winning on the battlefield and present a significant safety issue for the Army. One of the most promising areas for achieving better BH readiness results lies in improving readiness decision-making support for BH providers. The Walter Reed Army Institute of Research (WRAIR) has taken the lead in addressing this challenge by developing and empirically testing such tools. The results of the Behavioral Health Readiness Evaluation and Decision-Making Instrument (B-REDI) field study are herein described. Methods The B-REDI study received WRAIR Institutional Review Board approval, and BH providers across five U.S. Army Forces Command installations completed surveys from September 2018 to March 2019. The B-REDI tools/training were disseminated to 307 providers through random clinic assignments. Of these, 250 (81%) providers consented to participate and 149 (60%) completed both initial and 3-month follow-up surveys. Survey items included a wide range of satisfaction, utilization, and proficiency-level outcome measures. Analyses included examinations of descriptive statistics, McNemar’s tests pre-/post-B-REDI exposure, Z-tests with subgroup populations, and chi-square tests with demographic comparisons. Results The B-REDI resulted in broad, statistically significant improvements across the measured range of provider proficiency-level outcomes. Net gains in each domain ranged from 16.5% to 22.9% for knowledge/awareness (P = .000), from 11.1% to 15.8% for personal confidence (P = .001-.000), and from 6.2% to 15.1% for decision-making/documentation (P = .035-.002) 3 months following B-REDI initiation, and only one (knowledge) failed to maintain a statistically significant improvement in all of its subcategories. The B-REDI also received high favorability ratings (79%-97% positive) across a wide array of end-user satisfaction measures. Conclusions The B-REDI directly addresses several critical Army BH readiness challenges by providing tangible decision-making support solutions for BH providers. Providers reported high degrees of end-user B-REDI satisfaction and significant improvements in all measured provider proficiency-level domains. By effectively addressing the readiness decision-making challenges Army BH providers encounter, B-REDI provides the Army BH health care system with a successful blueprint to set the conditions necessary for providers to make more accurate and timely readiness determinations. This may ultimately reduce safety and mission failure risks enterprise-wide, and policymakers should consider formalizing and integrating the B-REDI model into current Army BH practice.

The Application of Multicriteria Decision Analysis Methods in Health Care: A Literature Review

2021 ◽  
pp. 0272989X2110190
Author(s):  
Ilyas Khan ◽  
Liliane Pintelon ◽  
Harry Martin
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Decision Analysis ◽  
Medical Devices ◽  
Hybrid Methods ◽  
Multicriteria Decision Analysis ◽  
Multicriteria Decision ◽  
Health Care Decision ◽  
Wide Range ◽  
Care Decision

Objectives The main objectives of this article are 2-fold. First, we explore the application of multicriteria decision analysis (MCDA) methods in different areas of health care, particularly the adoption of various MCDA methods across health care decision making problems. Second, we report on the publication trends on the application of MCDA methods in health care. Method PubMed was searched for literature from 1960 to 2019 in the English language. A wide range of keywords was used to retrieve relevant studies. The literature search was performed in September 2019. Articles were included only if they have reported an MCDA case in health care. Results and Conclusion The search yielded 8,318 abstracts, of which 158 fulfilled the inclusion criteria and were considered for further analysis. Hybrid methods are the most widely used methods in health care decision making problems. When it comes to single methods, analytic hierarchy process (AHP) is the most widely used method followed by TOPSIS (technique for order preference by similarity to ideal solution), multiattribute utility theory, goal programming, EVIDEM (evidence and value: impact on decision making), evidential reasoning, discrete choice experiment, and so on. Interestingly, the usage of hybrid methods has been high in recent years. AHP is most widely applied in screening and diagnosing and followed by treatment, medical devices, resource allocation, and so on. Furthermore, treatment, screening and diagnosing, medical devices, and drug development and assessment got more attention in the MCDA context. It is indicated that the application of MCDA methods to health care decision making problem is determined by the nature and complexity of the health care problem. However, guidelines and tools exist that assist in the selection of an MCDA method.

scholarly journals Goal-driven active learning

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Process ◽  
Real World ◽  
Imitation Learning ◽  
Learning Approaches ◽  
Wide Range ◽  
Fixed Set ◽  
Complex Decision Making ◽  
Complex Decision

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.

Computational Strategies for Analyzing Data in Gene Expression Microarray Experiments

2003 ◽  
Vol 01 (03) ◽  
pp. 541-586 ◽  
Author(s):  
Tero Aittokallio ◽  
Markus Kurki ◽  
Olli Nevalainen ◽  
Tuomas Nikula ◽  
Anne West ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Microarray Data ◽  
Microarray Data Analysis ◽  
Biological Research ◽  
Microarray Experiments ◽  
Dna Microarray Data ◽  
Open Questions ◽  
Analysis Technique ◽  
Wide Range

Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.

Perceived Affordances and Regret in Online Travel Agencies

2021 ◽  
pp. 004728752110149
Author(s):  
Hwirim Jo ◽  
Namho Chung ◽  
Sunyoung Hlee ◽  
Chulmo Koo
Keyword(s):  
Decision Making ◽  
Regulatory Focus ◽  
Group Analysis ◽  
Cognitive Effort ◽  
Wide Range ◽  
Impulsive Buying ◽  
Travel Agencies ◽  
Effective Decision Making ◽  
Online Travel ◽  
Online Travel Agencies

Despite the revolutionary system of online booking, the decision-making process for booking hotels is still very stressful for customers, who face much uncertainty. The wide range of products and great volume of information result in significant cognitive overload. Therefore, online travel agencies (OTAs) try to reduce customers’ cognitive effort requirements and to induce effective decision making by triggering potential actions through perceived affordance. This study aims to explore the influence of perceived affordance on purchase decisions and postpurchase emotion in the context of OTAs. The findings show that explicit affordance and hidden affordance significantly affect impulsive buying, thus resulting in postpurchase discomfort and regret. Additionally, the outcomes of a multiple group analysis revealed a significant moderating effect of regulatory focus orientation on impulsive buying and postpurchase regret during an overall purchase process involving OTAs.

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