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Bayesian Networks in Healthcare: Distribution by Medical Condition [article]

Scott McLachlan, Kudakwashe Dube, Graham A Hitman, Norman E Fenton, Evangelia Kyrimi
2020 arXiv   pre-print
Bayesian networks (BNs) have received increasing research attention that is not matched by adoption in practice and yet have potential to significantly benefit healthcare.  ...  This research seeks to identify and quantify the range of medical conditions for which healthcare-related BN models have been proposed, and the differences in approach between the most common medical conditions  ...  Acknowledgement SM, EK, GAH and NF acknowledge support from the EPSRC under project EP/P009964/1: PAMBAYESIAN: Patient Managed decision-support using Bayes Networks.  ... 
arXiv:2002.00224v2 fatcat:yprybxqlzvfe5no55gbtuz4fmm

Bayesian Networks in Healthcare: Distribution by Medical Condition

Scott McLachlan, Kudakwashe Dube, Graham A Hitman, Norman Fenton, Evangelia Kyrimi
2020 Artificial Intelligence in Medicine  
Bayesian networks (BNs) have received increasing research attention that is not matched by adoption in practice and yet have potential to significantly benefit healthcare.  ...  This research seeks to identify and quantify the range of medical conditions for which healthcare-related BN models have been proposed, and the differences in approach between the most common medical conditions  ...  Acknowledgement SM, EK, GAH and NF acknowledge support from the EPSRC under project EP/P009964/1: PAMBAYESIAN: Patient Managed decision-support using Bayes Networks.  ... 
doi:10.1016/j.artmed.2020.101912 pmid:32828451 fatcat:ukesjlttizh7vpv5zn74pmapvi

Using Bayesian Networks for Risk Assessment in Healthcare System [chapter]

Bouchra Zoullouti, Mustapha Amghar, Sbiti Nawal
2019 Bayesian Networks [Working Title]  
It uses Bayesian networks for quantitative risk analysis in the hospital.  ...  The second approach uses the fuzzy Bayesian network to model and analyze risk.  ...  Bayesian Networks -Advances and Novel Applications Table 14 . 14 Conditional occurrence probability of "patient injury".Using Bayesian Networks for Risk Assessment in Healthcare System http://dx.doi.org  ... 
doi:10.5772/intechopen.80464 fatcat:hpxri2xscfbpxguuliltnvd4te

A Comprehensive Scoping Review of Bayesian Networks in Healthcare: Past, Present and Future [article]

Evangelia Kyrimi, Scott McLachlan, Kudakwashe Dube, Mariana R. Neves, Ali Fahmi, Norman Fenton
2020 arXiv   pre-print
No comprehensive review of Bayesian networks (BNs) in healthcare has been published in the past, making it difficult to organize the research contributions in the present and identify challenges and neglected  ...  The review shows that: (1) BNs in healthcare are not used to their full potential; (2) a generic BN development process is lacking; (3) limitations exists in the way BNs in healthcare are presented in  ...  ACKNOWLEDGMENTS EK, SM, MRN, AF and NF acknowledge support from the Engineering and Physical Sciences Research Council (EPSRC) under project EP/P009964/1: PAMBAYESIAN: Patient Managed decision-support using Bayes Networks  ... 
arXiv:2002.08627v2 fatcat:r73w5ic5nzg2vc62poqfbokka4

Exploiting causal functional relationships in Bayesian network modelling for personalised healthcare

Marina Velikova, Josien Terwisscha van Scheltinga, Peter J.F. Lucas, Marc Spaanderman
2014 International Journal of Approximate Reasoning  
We argue that Bayesian networks offer appropriate technology for the successful modelling of medical problems, including the personalisation of healthcare.  ...  Bridging the gap between the theory of Bayesian networks and solving an actual problem is still a big challenge and this is in particular true for medical problems, where such a gap is clearly evident.  ...  Acknowledgement The work has been funded by the STITPRO Foundation, The Netherlands.  ... 
doi:10.1016/j.ijar.2013.03.016 fatcat:6f3tfsizc5btpe7nz3fdhspksy

Bayesian Networks in Healthcare: the chasm between research enthusiasm and clinical adoption [article]

Evangelia Kyrimi, Scott McLachlan, Kudakwashe Dube, Norman Fenton
2020 medRxiv   pre-print
Problem: Bayesian Networks (BN) can address real-world decision-making problems, and there is enormous and rapidly increasing interest in their use in healthcare.  ...  Yet, despite thousands of BNs in healthcare papers published yearly, evidence of their adoption in practice is extremely limited and there is no consensus on why.  ...  EK proposed the preliminary framework, which was refined by SM, KD and NF. EK and SM conducted the preliminary review. KD and NF supervised the research.  ... 
doi:10.1101/2020.06.04.20122911 fatcat:zi7qfy6tvbhknlt57aqvweayli

Towards Bayesian-Based Trust Management for Insider Attacks in Healthcare Software-Defined Networks

Weizhi Meng, Kim-Kwang Raymond Choo, Steven Furnell, Athanasios V. Vasilakos, Christian W. Probst
2018 IEEE Transactions on Network and Service Management  
The medical industry is increasingly digitalized and Internet-connected (e.g., Internet of Medical Things), and when deployed in an Internet of Medical Things environment, softwaredefined networks (SDN  ...  Based on the survey findings, we develop a trust-based approach based on Bayesian inference to figure out malicious devices in a healthcare environment.  ...  ACKNOWLEDGMENT We would like to thank all surveyed healthcare managers and IT administrators from the relevant hospitals and clinics in Hong Kong, China and Singapore for their great support and helpful  ... 
doi:10.1109/tnsm.2018.2815280 fatcat:vrpjgsms4jcjnmkletvprs3rhi

A Review of Dynamic Bayesian Network Techniques with Applications in Healthcare Risk Modelling

Mohsen Mesgarpour, Thierry Chaussalet, Salma Chahed, Marc Herbstritt
2014 Student Conference on Operational Research  
The main focus of this study is to provide a review of major time-varying Dynamic Bayesian Network techniques with applications in healthcare predictive risk modelling.  ...  Dynamic Bayesian Network techniques can represent complex correlations models and include small probabilities into the solution.  ...  Bayesian Networks There are two main approaches in incorporating stochastic models to the statistical modelling: discriminative (conditional distribution model) and generative (joint probability model)  ... 
doi:10.4230/oasics.scor.2014.89 dblp:conf/scor/MesgarpourCC14 fatcat:syczsuh7l5cnzcqbqzxrlpfk74

Identifying High-Risk Factors of Depression in Middle-Aged Persons with a Novel Sons and Spouses Bayesian Network Model

Francis Joseph Costello, Cheong Kim, Chang Min Kang, Kun Chang Lee
2020 Healthcare  
Therefore, our proposed method can help healthcare decision-makers comprehend changes in depression status by employing what-if queries towards a target individual.  ...  The proposed method is the Sons and Spouses Bayesian network model, which is an extended version of conventional TAN (Tree-Augmented Naive Bayesian Network).  ...  Acknowledgments: The authors would like to give thanks to Dong Eun Lee for his valuable contribution in helping to obtain the dataset as well as his invaluable insights into obtaining reliable results.  ... 
doi:10.3390/healthcare8040562 pmid:33333799 fatcat:amoapzqbgbftplycqcorkx2rfq

Prevalence of tuberculosis infection in healthcare workers of the public hospital network in Medellín, Colombia: a Bayesian approach

J. OCHOA, A. L. LEÓN, I. C. RAMÍREZ, C. M. LOPERA, E. BERNAL, M. P. ARBELÁEZ
2017 Epidemiology and Infection  
SUMMARY A latent tuberculosis infection (LTBI) prevalence survey was conducted using tuberculin skin test (TST) and Quantiferon test (QFT) in 1218 healthcare workers (HCWs) in Medellín, Colombia.  ...  In order to improve the prevalence estimates, a latent class model was built using a Bayesian approach with informative priors on the sensitivity and specificity of the TST.  ...  In particular, we thank all the HCWs who participated in the study.  ... 
doi:10.1017/s0950268816003150 pmid:28065210 fatcat:qwfgq5wxybbhjpohfcveswpn6q

Learning to Address Health Inequality in the United States with a Bayesian Decision Network [article]

Tavpritesh Sethi, Anant Mittal, Shubham Maheshwari, Samarth Chugh
2018 arXiv   pre-print
We learn an ensemble-averaged structure, draw inferences using the joint probability distribution and extend it to a Bayesian Decision Network for identifying policy actions.  ...  In this work, we reveal actionable interventions for decreasing the longevity-gap in the United States by analyzing a County-level data resource containing healthcare, socio-economic, behavioral, education  ...  Rakesh Lodha, Department of Pediatrics, All India Institute of Medical Sciences, New Delhi, India.  ... 
arXiv:1809.09215v2 fatcat:y5nv57pkhncvrlqpur4vevbjcq

Incorporating Interpretable Output Constraints in Bayesian Neural Networks [article]

Wanqian Yang, Lars Lorch, Moritz A. Graule, Himabindu Lakkaraju, Finale Doshi-Velez
2021 arXiv   pre-print
We introduce a novel probabilistic framework for reasoning with such constraints and formulate a prior that enables us to effectively incorporate them into Bayesian neural networks (BNNs), including a  ...  Unlike typical BNN inference in uninterpretable parameter space, OC-BNNs widen the range of functional knowledge that can be incorporated, especially for model users without expertise in machine learning  ...  Performing Bayesian inference (11) on deep neural networks Φ W (with weights and biases parametrized by W) results in a Bayesian neural network (BNN).  ... 
arXiv:2010.10969v2 fatcat:ef4swnzfcjd2nkvmrajyklt5cq

Quantitative risk analysis in radiotherapy using Bayesian networks [chapter]

A Reitz, E Levrat, J Pétin
2013 Safety, Reliability and Risk Analysis  
Each fraction is distributed over a set of beams aiming tu-Quantitative risk analysis in radiotherapy using Bayesian networks A. Reitz, E. Levrat, J-F.  ...  Bayesian Networks addresses all those problems. But, there is no structured method to develop a risk model with Bayesian network.  ... 
doi:10.1201/b15938-368 fatcat:aluqcr7f5feelpnvgyvekmstbu

A Bayesian Hierarchical Network for Combining Heterogeneous Data Sources in Medical Diagnoses [article]

Claire Donnat, Nina Miolane, Freddy Bunbury, Jack Kreindler
2020 arXiv   pre-print
Computer-Aided Diagnosis has shown stellar performance in providing accurate medical diagnoses across multiple testing modalities (medical images, electrophysiological signals, etc.).  ...  Our Bayesian formalism is essential in (a) flexibly combining these heterogeneous data sources and their corresponding levels of uncertainty, (b) quantifying the degree of confidence associated with a  ...  Bayesian networks have thus been implemented in a variety of contexts to integrate clinical data and laboratory results, and diagnose conditions ranging from pyloric stenosis to acute appendicitis [26  ... 
arXiv:2007.13847v2 fatcat:zvzktk7rpnaqrcshhlruk67kya

An Ontology Driven and Bayesian Network Based Cardiovascular Decision Support Framework [chapter]

Kamran Farooq, Amir Hussain, Stephen Leslie, Chris Eckl, Calum MacRae, Warner Slack
2012 Lecture Notes in Computer Science  
We have also utilized a Bayesian Network (BN) approach for modelling clinical uncertainty in the Electronic Healthcare Records (EHRs).  ...  In this paper, we present a cardiovascular decision support framework based on key ontology engineering principles and a Bayesian Network.  ...  Bayesian Network for Uncertainty Modelling in the Electronic Healthcare Records We have developed a Bayesian network a model as presented in Fig 4, using the Electronic Healthcare Records generated in  ... 
doi:10.1007/978-3-642-31561-9_4 fatcat:5xbsi5mryveddlxi7gzyck3ygy
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