ArticleExpert systems with applications2016
Integrating Expert Knowledge with Data in Bayesian Networks:
Article in Expert systems with applications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Causal Modelling for Supporting Planning and Management of Mental Health Services and Systems: A Systematic Review.International journal of environmental research and public health · 2019Pooled it
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- Probabilistic graphical model for the evaluation of the emotional and dramatic personality disorders.Frontiers in psychology · 2022Article
- A Review on Human-AI Interaction in Machine Learning and Insights for Medical Applications.International journal of environmental research and public health · 2021Review
- Bayesian networks for supply chain risk, resilience and ripple effect analysis: A literature review.Expert systems with applications · 2020Article
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- A Framework for Global Twenty-First Century Scenarios and Models of Biological Invasions.Bioscience · 2019Article
- Bayesian Approach to Predicting Acute Appendicitis Using Ultrasonographic and Clinical Variables in Children.Healthcare informatics research · 2019Article
- A data-driven and practice-based approach to identify risk factors associated with hospital-acquired falls: Applying manual and semi- and fully-automated methods.International journal of medical informatics · 2019Observational
- The future of the London Buy-To-Let property market: Simulation with temporal Bayesian Networks.PloS one · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
When developing a causal probabilistic model, i.e. a Bayesian network (BN), it is common to incorporate expert knowledge of factors that are important for decision analysis but where historical data are unavailable or difficult to obtain. This paper focuses on the problem whereby the distribution of some continuous variable in a BN is known from data, but where we wish to explicitly model the impact of some additional expert variable (for which there is expert judgment but no data). Because the statistical outcomes are already influenced by the causes an expert might identify as variables missing from the dataset, the incentive here is to add the expert factor to the model in such a way that the distribution of the data variable is preserved when the expert factor remains unobserved. We provide a method for eliciting expert judgment that ensures the
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.