Bayesian network modeling: A case study of an epidemiologic system analysis of cardiovascular risk

Fuster-Parra, Pilar; Tauler, Pedro; Bennasar‐Veny, Miquel; Ligęza, Antoni; López-González, Arturo; Aguiló, Antoni

doi:10.1016/j.cmpb.2015.12.010

Cited by 80 publications

(47 citation statements)

References 52 publications

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“…The initial Bayesian network models were developed after data cleaning and missing data assessment [23]. The following steps were taken to finalize the model.…”

Section: Methodsmentioning

confidence: 99%

A network perspective on patient experiences and health status: the Medical Expenditure Panel Survey 2004 to 2011

Chao

Scutari

et al. 2017

BMC Health Serv Res

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BackgroundThere is a growing emphasis on the need to engage patients in order to improve the quality of health care and improve health outcomes. However, we are still lacking a comprehensive understanding on how different measures of patient experiences interact with one another or relate to health status. This study takes a network perspective to 1) study the associations between patient characteristics and patient experience in health care and 2) identify factors that could be prioritized to improve health status.MethodsThis study uses data from the two-year panels from the Medical Expenditure Panel Survey (MEPS) initiated between 2004 and 2011 in the United States. The 88 variables regarding patient health and experience with health care were identified through the MEPS documentation. Sex, age, race/ethnicity, and years of education were also included for analysis. The bnlearn package within R (v3.20) was used to 1) identify the structure of the network of variables, 2) assess the model fit of candidate algorithms, 3) cross-validate the network, and 4) fit conditional probabilities with the given structure.ResultsThere were 51,023 MEPS interviewees aged 18 to 85 years (mean = 44, 95% CI = 43.9 to 44.2), with years of education ranging from 1 to 19 (mean = 7.4, 95% CI = 7.40 to 7.46). Among all, 55% and 74% were female and white, respectively. There were nine networks identified and 17 variables not linked to others, including death in the second years, sex, entry years to the MEPS, and relations of proxies. The health status in the second years was directly linked to that in the first years. The health care ratings were associated with how often professionals listened to them and whether professionals’ explanation was understandable.ConclusionsIt is feasible to construct Bayesian networks with information on patient characteristics and experiences in health care. Network models help to identify significant predictors of health care quality ratings. With temporal relationships established, the structure of the variables can be meaningful for health policy researchers, who search for one or a few key priorities to initiate interventions or health care quality improvement programs.Electronic supplementary materialThe online version of this article (doi:10.1186/s12913-017-2496-5) contains supplementary material, which is available to authorized users.

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“…The initial Bayesian network models were developed after data cleaning and missing data assessment [23]. The following steps were taken to finalize the model.…”

Section: Methodsmentioning

confidence: 99%

A network perspective on patient experiences and health status: the Medical Expenditure Panel Survey 2004 to 2011

Chao

Scutari

et al. 2017

BMC Health Serv Res

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Section: Bayesian Methods Solutionsmentioning

confidence: 99%

“…Bayesian Networks is also probabilistic causal networks also known as Belief Networks are the Artificial Intelligence framework for uncertainty supervision which is contrary to deterministic approach to understand phenomena [26]. Although it was published in 1763 the techniques apply in health management and medicine decision-support systems are quite recent [24] and widely applied in clinical support decision [26]. Bayesian method offers instinctive, meaningful, professional and rational inferential analysis which gives the capability to solve complex situations given the priori distribution in addition to the dataset, thus making decisions easier to clarify and explain [25].…”

Section: Bayesian Methods Solutionsmentioning

confidence: 99%

A Review of Predictive Analytic Applications of Bayesian Network

Yusof¹,

Mokhtar²

2016

International Journal on Advanced Science, Engineering and Information Technology

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Malware can be defined as malicious software that infiltrates a network and computer host in a variety of ways, from software flaws to social engineering. Due to the polymorphic and stealth nature of malware attacks, a signature-based analysis that is done statically is no longer sufficient to solve such a problem. Therefore, a behavioral or anomalous analysis will provide a more dynamic approach for the solution. However, recent studies have shown that current behavioral methods at the network-level have several issues such as the inability to predict zero-day attacks, high-level assumptions, non-inferential analysis and performance issues. Other than performance issues, this study has identified common scientific characteristics which are reduced parameter, θ and lack of priori information p(θ) that causes the problems. Previous methods were proposed to address the problem, however, were still unable to resolve the stated scientific hitches. Due to the shortcomings, the Bayesian Network in terms of its probabilistic modeling would be the best method to deal with the stated scientific glitches which also have been proven in the area of Clinical Expert Systems, Artificial Intelligence, and Pattern Recognition. This study will critically review the predictive analytic applications of Bayesian Network model in different research domain such as Clinical Expert Systems, Artificial Intelligence, and Pattern Recognition and discover any potential approach available in the domain of Computer Networks. Based on the review, this paper has identified several Bayesian Network properties which have been used to overcome the abovementioned problems. Those properties will be applied in future studies to model the Behavioral Malware Predictive Analytics.

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“…As a machine-learning algorithm algorithm, Bayesian networks (BNs) produce an intuitive, transparent, graphical representation of the interrelationships between factors, and can accurately reveal potential overall information [11]. The non-strict requirements for statistical assumptions in BNs modeling also made it of special significance in epidemiological studies [12].…”

mentioning

confidence: 99%

Application of group LASSO regression based Bayesian networks in risk factors exploration and disease prediction for acute kidney injury in hospitalized patients with hematologic malignancies

Chen

Wang

et al. 2019

Preprint

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Background: This study aims to explore a novel machine-learning algorithm, Bayesian networks (BNs), to delineate the interrelationships between acute kidney injury (AKI) and its associated risk factors among patients with hematologic malignancies (HM), to assess the prediction ability of BNs model, and to infer the probability of AKI under different clinical settings. Methods: From 1 October 2014 to 30 September 2015, 2501 hospitalized patients diagnosed with HM in Zhongshan Hospital, Fudan University, Shanghai of China, were recruited in this retrospective study. Data on demographics, comorbidities, and baseline clinical lab records were exported from the electronic medical records. The group-LASSO (gLASSO) regression was performed to select the candidate predictors of AKI, which were further presented in BNs analysis for interrelationship exploration and disease prediction. Results: Among 2395 eligible patients, 370 episodes were diagnosed with AKI (15.4%). Patients with multiple myeloma (24.1%) and leukemia (23.9%) shared a higher AKI incidence than lymphoma (13.4%). Screened by the gLASSO regression, variables as age, gender, diabetes, hemopathy category, anti-tumor treatment, hemoglobin, serum creatinine (SCr), estimated glomerular filtration rate (eGFR), serum uric acid, serum sodium and potassium level were found with significant associations with AKI occurrence. BNs model revealed a complex interrelationship between these factors, among which there were direct connections between AKI and age, hemoglobin, eGFR, serum sodium and potassium. Moreover, HM category and anti-tumor treatment were indirectly linked to AKI through hemoglobin and eGFR; diabetes was connected with AKI through serum sodium level. BNs inferences indicated that when poor GFR, anemia and hyponatremia occurred simultaneously, the probability of AKI might reach 78.5%. The area under the receiver operating characteristic curve (AUC) of BNs model was 0.835, higher than that in logistic score model (0.763) and showed a robust performance in 10-fold cross-validation. Conclusion: AKI is common in HM hospitalized patients and is affected by multiple risk factors. The application of Bayesian networks and gLASSO regression can provide a novel strategy to explore the potential risk factors integrally and deep into their interrelationships, in accordance with the tide of e-alert and big-data for AKI early detection.

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Bayesian network modeling: A case study of an epidemiologic system analysis of cardiovascular risk

Cited by 80 publications

References 52 publications

A network perspective on patient experiences and health status: the Medical Expenditure Panel Survey 2004 to 2011

A network perspective on patient experiences and health status: the Medical Expenditure Panel Survey 2004 to 2011

A Review of Predictive Analytic Applications of Bayesian Network

Application of group LASSO regression based Bayesian networks in risk factors exploration and disease prediction for acute kidney injury in hospitalized patients with hematologic malignancies

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