Prediction of the Risk of Osteoporotic Fractures Using Bayesian Belief Networks

Dmitriev, G. A.; Vetrov, A. N.; Alfakih, Amani

doi:10.1007/s10527-017-9677-8

Cited by 1 publication

(1 citation statement)

References 3 publications

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“…BBNs have been used to address problems in supply chain management (Garvey et al , 2015; Pai et al , 2003; Qazi et al , 2017; Amundson et al , 2014); low-probability/high consequence events such as transportation accidents (Luxhoj and Coit, 2006; Zhang et al , 2016; Martins and Maturana, 2013); environmental management (Pagano et al , 2018; Phan et al , 2016; Landuyt et al , 2013); criminal law (McDermott and Aitken, 2017); healthcare (Dmitriev et al , 2014; Liu et al , 2012; Steele et al , 2012); manufacturing (Buyukozkan et al , 2015; Quintana, 14 September 2012); service operations (Kekolahti and Karikoski, 2013); education (Millan et al , 2010); and other industries.…”

Section: Literature Surveymentioning

confidence: 99%

Ishikawa diagrams and Bayesian belief networks for continuous improvement applications

Rodgers

Oppenheim

2019

TQM

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Purpose In continuous improvement (CI) projects, cause-and-effect diagrams are used to qualitatively express the relationship between a given problem and its root causes. However, when data collection activities are limited, and advanced statistical analyses are not possible, practitioners need to understand causal relationships. The paper aims to discuss these issues. Design/methodology/approach In this research, the authors present a framework that combines cause-and-effect diagrams with Bayesian belief networks (BBNs) to estimate causal relationships in instances where formal data collection/analysis activities are too costly or impractical. Specifically, the authors use cause-and-effect diagrams to create causal networks, and leverage elicitation methods to estimate the likelihood of risk scenarios by means of computer-based simulation. Findings This framework enables CI practitioners to leverage qualitative data and expertise to conduct in-depth statistical analysis in the event that data collection activities cannot be fully executed. Furthermore, this allows CI practitioners to identify critical root causes of a given problem under investigation before generating solutions. Originality/value This is the first framework that translates qualitative insights from a cause-and-effect diagram into a closed-form relationship between inputs and outputs by means of BBN models, simulation and regression.

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