Approximating non-Gaussian Bayesian networks using minimum information vine model with applications in financial modelling

Chatrabgoun, Omid; Hosseinian-Far, Amin; Chang, Victor; Stocks, Nigel G.; Daneshkhah, Alireza

doi:10.1016/j.jocs.2017.09.002

Cited by 24 publications

(14 citation statements)

References 26 publications

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“…Applications of BN methods are found in a growing number of disciplines and policies [35,55]. BNs are particularly useful for evaluation because of their capability of classification based on observations.…”

Section: Methodsmentioning

confidence: 99%

Predicting mortality, duration of treatment, pulmonary embolism and required ceiling of ventilatory support for COVID-19 inpatients: A Machine-Learning Approach

Vepa

Saleem

Rakhshan

et al. 2021

Preprint

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Within the UK, COVID-19 has contributed towards over 103,000 deaths. Multiple risk factors for COVID-19 have been identified including various demographics, co-morbidities, biochemical parameters, and physical assessment findings. However, using this vast data to improve clinical care has proven challenging. The main aim of this paper is to develop a reliable, multivariable predictive model for COVID-19 in-patient outcomes, to aid risk-stratification and earlier clinical decision-making. Anonymized data regarding 44 independent predictor variables of 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case-controlled analysis. Primary outcomes included inpatient mortality, level of ventilatory support and oxygen therapy required, and duration of inpatient treatment. Secondary pulmonary embolism was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were created using Bayesian Networks, and cross-validated. The developed Bayesian machine learning models were able to predict, using feature selected risk factors, the probability of inpatient mortality (F1 score 83.7%, PPV 82%, NPV 67.9%); level of ventilatory support required (F1 score varies from 55.8% "High-flow Oxygen level" to 71.5% "ITU-Admission level"); duration of inpatient treatment (varies from 46.7% for greater less than or equal 2 days but<3 days" to 69.8% "less than or equal 1 day"); and risk of pulmonary embolism sequelae (F1 score 85.8%, PPV of 83.7%, and NPV of 80.9% ). Overall, our findings demonstrate reliable, multivariable predictive models for 4 outcomes, that utilize readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as clinical decision-making tools.

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Section: Methodsmentioning

confidence: 99%

Predicting mortality, duration of treatment, pulmonary embolism and required ceiling of ventilatory support for COVID-19 inpatients: A Machine-Learning Approach

Vepa

Saleem

Rakhshan

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…This technique is becoming popular for aiding decision-making in several domains due to the evolution of computational capacity, which makes possible the calculation of complex BN [ 44 ]. Applications of BN methods are found in a growing number of disciplines and policies [ 14 , 41 , 45 , 46 ].…”

Section: Evaluation Methods: Probabilistic Graphical Modelsmentioning

confidence: 99%

Economic Evaluation of Mental Health Effects of Flooding Using Bayesian Networks

Sedighi

Varga²,

Hosseinian-Far

et al. 2021

IJERPH

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The appraisal of appropriate levels of investment for devising flooding mitigation and to support recovery interventions is a complex and challenging task. Evaluation must account for social, political, environmental and other conditions, such as flood state expectations and local priorities. The evaluation method should be able to quickly identify evolving investment needs as the incidence and magnitude of flood events continue to grow. Quantification is essential and must consider multiple direct and indirect effects on flood related outcomes. The method proposed is this study is a Bayesian network, which may be used ex-post for evaluation, but also ex-ante for future assessment, and near real-time for the reallocation of investment into interventions. The particular case we study is the effect of flood interventions upon mental health, which is a gap in current investment analyses. Natural events such as floods expose people to negative mental health disorders including anxiety, distress and post-traumatic stress disorder. Such outcomes can be mitigated or exacerbated not only by state funded interventions, but by individual and community skills and experience. Success is also dampened when vulnerable and previously exposed victims are affected. Current measures evaluate solely the effectiveness of interventions to reduce physical damage to people and assets. This paper contributes a design for a Bayesian network that exposes causal pathways and conditional probabilities between interventions and mental health outcomes as well as providing a tool that can readily indicate the level of investment needed in alternative interventions based on desired mental health outcomes.

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“…When the available data are not appropriate, more challenges will arise in terms of uncertainty [ 34 ]. A BN is a probabilistic graphical model which is used to represent knowledge about an uncertain domain [ 35 ]. Each random variable (a variable whose possible values are outcomes of a random phenomenon) is represented by a node in the BN.…”

Section: Bayesian Networkmentioning

confidence: 99%

Evaluating the Bovine Tuberculosis Eradication Mechanism and Its Risk Factors in England’s Cattle Farms

Sedighi

Varga²

2021

IJERPH

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Controlling bovine tuberculosis (bTB) disease in cattle farms in England is seen as a challenge for farmers, animal health, environment and policy-makers. The difficulty in diagnosis and controlling bTB comes from a variety of factors: the lack of an accurate diagnostic test which is higher in specificity than the currently available skin test; isolation periods for purchased cattle; and the density of active badgers, especially in high-risk areas. In this paper, to enable the complex evaluation of bTB disease, a dynamic Bayesian network (DBN) is designed with the help of domain experts and available historical data. A significant advantage of this approach is that it represents bTB as a dynamic process that evolves periodically, capturing the actual experience of testing and infection over time. Moreover, the model demonstrates the influence of particular risk factors upon the risk of bTB breakdown in cattle farms.

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Approximating non-Gaussian Bayesian networks using minimum information vine model with applications in financial modelling

Cited by 24 publications

References 26 publications

Predicting mortality, duration of treatment, pulmonary embolism and required ceiling of ventilatory support for COVID-19 inpatients: A Machine-Learning Approach

Predicting mortality, duration of treatment, pulmonary embolism and required ceiling of ventilatory support for COVID-19 inpatients: A Machine-Learning Approach

Economic Evaluation of Mental Health Effects of Flooding Using Bayesian Networks

Evaluating the Bovine Tuberculosis Eradication Mechanism and Its Risk Factors in England’s Cattle Farms

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