Interactive structural learning of Bayesian networks

Sucar, Luis Enrique; Martínez-Arroyo, Miriam

doi:10.1016/s0957-4174(98)00050-5

Cited by 12 publications

(5 citation statements)

References 11 publications

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“…The problem of discovering the causal structure (Landsheer, 2010;Scheines, Spirtes, Glymour, Meek, & Richardson, 1998) increases with the number of features (Buntine, 1996;Cheng, Greiner, Kelly, Bell, & Liu, 2002;Sucar & Martínez-Arroyo, 1998), e.g., for each pair of features, X and Y there are four possible kinds of connections: X ? Y, X Y, X M Y (a bidirectional connection between X and Y), X Y (X and Y are not connected).…”

Section: Structural Learningmentioning

confidence: 99%

Team performance and collective efficacy in the dynamic psychology of competitive team: A Bayesian network analysis

Fuster-Parra

García-Más

Ponseti

et al. 2015

Human Movement Science

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Section: Structural Learningmentioning

confidence: 99%

Team performance and collective efficacy in the dynamic psychology of competitive team: A Bayesian network analysis

Fuster-Parra

García-Más

Ponseti

et al. 2015

Human Movement Science

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“…The problem of discovering the causal structure increases with the number of variables (46)(47)(48). Table 1 shows a description of the 12 variables considered.…”

Section: Structural Learningmentioning

confidence: 99%

Identifying risk factors of developing type 2 diabetes from an adult population with initial prediabetes using a Bayesian network

Fuster-Parra

Yáñez

López-González

et al. 2023

Front. Public Health

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BackgroundIt is known that people with prediabetes increase their risk of developing type 2 diabetes (T2D), which constitutes a global public health concern, and it is associated with other diseases such as cardiovascular disease.MethodsThis study aimed to determine those factors with high influence in the development of T2D once prediabetes has been diagnosed, through a Bayesian network (BN), which can help to prevent T2D. Furthermore, the set of features with the strongest influences on T2D can be determined through the Markov blanket. A BN model for T2D was built from a dataset composed of 12 relevant features of the T2D domain, determining the dependencies and conditional independencies from empirical data in a multivariate context. The structure and parameters were learned with the bnlearn package in R language introducing prior knowledge. The Markov blanket was considered to find those features (variables) which increase the risk of T2D.ResultsThe BN model established the different relationships among features (variables). Through inference, a high estimated probability value of T2D was obtained when the body mass index (BMI) was instantiated to obesity value, the glycosylated hemoglobin (HbA1c) to more than 6 value, the fatty liver index (FLI) to more than 60 value, physical activity (PA) to no state, and age to 48–62 state. The features increasing T2D in specific states (warning factors) were ranked.ConclusionThe feasibility of BNs in epidemiological studies is shown, in particular, when data from T2D risk factors are considered. BNs allow us to order the features which influence the most the development of T2D. The proposed BN model might be used as a general tool for prevention, that is, to improve the prognosis.

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“…Discovering the structure of a BN is a problem which difficulty increases with the number of variables [47][48][49] . It is known that the number of different structures, f (n), grows more than exponentially in the number of variables, the following efficiently computable recursive function 50 is given in Equation ( 2):…”

Section: Structural Learningmentioning

confidence: 99%

Identifying the interplay between protective measures and settings on the SARS-CoV-2 transmission using a Bayesian network

Fuster-Parra,

Huguet-Torres,

Castro-Sánchez

et al. 2023

Preprint

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During the COVID-19 pandemic, primary healthcare nurses played a crucial role in contact tracing to minimize the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spread. However, contact tracing data were underutilized to explore the potential ways of virus transmission. Research on community spread, the effectiveness of individual protective measures, and the impact of different exposure environments is limited. This study focused on the transmission of SARS-CoV-2, aiming to describe the interplay between prevention measures and characteristics of exposure settings, using the data collated from a national SARS-COV-2 contact tracing programme to build a Bayesian network (BN) with 11 relevant features, which could potentially determine features with the strongest influence in SARS-CoV-2 transmission. BNs allow predictions of new scenarios when hypothetical information is introduced, which makes them of special interest in epidemiological studies. In our results, ventilation of the setting and time of exposure were the main factors for SARS-CoV-2 transmission. BN analysis can help decision-makers refine public health campaigns and priorise resources for individuals at higher risk, and also provides personalized guidance in specific protective measures for different settings or environments.

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Interactive structural learning of Bayesian networks

Cited by 12 publications

References 11 publications

Team performance and collective efficacy in the dynamic psychology of competitive team: A Bayesian network analysis

Team performance and collective efficacy in the dynamic psychology of competitive team: A Bayesian network analysis

Identifying risk factors of developing type 2 diabetes from an adult population with initial prediabetes using a Bayesian network

Identifying the interplay between protective measures and settings on the SARS-CoV-2 transmission using a Bayesian network

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