Interactive Decision Support: A Framework to Improve Diagnostic Processes of Cancerous Diseases Using Bayesian Networks

2018 ◽  
Author(s):  
Patrick Philipp ◽  
Jurgen Beyerer ◽  
Sebastian Robert ◽  
Dirk Hempel
Keyword(s):  
Decision Support ◽  
Bayesian Networks ◽  
Interactive Decision Support

Application of Bayesian for the Situation Assessment of Sea-Battlefield

2013 ◽  
Vol 346 ◽  
pp. 135-139 ◽  
Author(s):  
Yong Tao Yu ◽  
Ying Ding ◽  
Zheng Xi Ding
Keyword(s):  
Decision Support ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Conditional Probability ◽  
Assessment Model ◽  
Situation Assessment ◽  
Network Characteristics ◽  
Specific Assessment

The sea-battlefield situation is dynamic and how efficient sea-battlefield situation assessment is a major problem facing operational decision support. According to research based on Bayesian networks Sea-battlefield situation assessment, first constructed sea-battlefield situation assessment Bayesian network; followed by specific assessment objectives, to simplify creating sub Bayesian assessment model; once again based on Bayesian network characteristics to determine each node probability formula; finally, according to the formula for solving the edge of the probability and the conditional probability of each node, sea-battlefield situation assessment.

Decision support analysis for safety control in complex project environments based on Bayesian Networks

2013 ◽  
Vol 40 (11) ◽  
pp. 4273-4282 ◽  
Author(s):  
Limao Zhang ◽  
Xianguo Wu ◽  
Lieyun Ding ◽  
Miroslaw J. Skibniewski ◽  
Y. Yan
Keyword(s):  
Decision Support ◽  
Bayesian Networks ◽  
Safety Control ◽  
Complex Project

scholarly journals Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal

Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2642 ◽  
Author(s):  
Thuc D. Phan ◽  
James C. R. Smart ◽  
Ben Stewart-Koster ◽  
Oz. Sahin ◽  
Wade L. Hadwen ◽  
...  
Keyword(s):  
Climate Change ◽  
Decision Support ◽  
Resource Management ◽  
Bayesian Networks ◽  
Water Resource ◽  
Water Resource Management ◽  
Decision Support Tools ◽  
Management Measures ◽  
Support Tools ◽  
Economic Stressors

Bayesian networks (BNs) are widely implemented as graphical decision support tools which use probability inferences to generate “what if?” and “which is best?” analyses of potential management options for water resource management, under climate change and socio-economic stressors. This paper presents a systematic quantitative literature review of applications of BNs for decision support in water resource management. The review quantifies to what extent different types of data (quantitative and/or qualitative) are used, to what extent optimization-based and/or scenario-based approaches are adopted for decision support, and to what extent different categories of adaptation measures are evaluated. Most reviewed publications applied scenario-based approaches (68%) to evaluate the performance of management measures, whilst relatively few studies (18%) applied optimization-based approaches to optimize management measures. Institutional and social measures (62%) were mostly applied to the management of water-related concerns, followed by technological and engineered measures (47%), and ecosystem-based measures (37%). There was no significant difference in the use of quantitative and/or qualitative data across different decision support approaches (p = 0.54), or in the evaluation of different categories of management measures (p = 0.25). However, there was significant dependence (p = 0.076) between the types of management measure(s) evaluated, and the decision support approaches used for that evaluation. The potential and limitations of BN applications as decision support systems are discussed along with solutions and recommendations, thereby further facilitating the application of this promising decision support tool for future research priorities and challenges surrounding uncertain and complex water resource systems driven by multiple interactions amongst climatic and non-climatic changes.

Decision support system for Warfarin therapy management using Bayesian networks

2013 ◽  
Vol 55 (2) ◽  
pp. 488-498 ◽  
Author(s):  
Barbaros Yet ◽  
Kaveh Bastani ◽  
Hendry Raharjo ◽  
Svante Lifvergren ◽  
William Marsh ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Bayesian Networks ◽  
Support System ◽  
Warfarin Therapy ◽  
Therapy Management
Sign in / Sign up

Export Citation Format

Share Document