The Paper Feed

A feed of Bayesian network related papers, articles, books and research that we happen across and find of interest

Factors influencing sedentary behaviour: A system based analysis using Bayesian networks within DEDIPAC

Buck, C. and Loyen, A. and Foraita, R. and Van Cauwenberg, J. and De Craemer, M. and Mac Donncha, C. and Oppert, J.M. and Brug, J. and Lien, N. and Cardon, G. and Pigeot, I. and Chastin, S. and on behalf of the DEDIPAC consortium
Decreasing sedentary behaviour (SB) has emerged as a public health priority since prolonged sitting increases the risk of non-communicable diseases. Mostly, the independent association of factors with SB has been investigated, although lifestyle behaviours are conditioned by interdependent factors. Within the DEDIPAC Knowledge Hub, a system of sedentary behaviours (SOS)-framework was created to take interdependency among multiple factors into account. The SOS framework is based on a system approach and was developed by combining evidence synthesis and expert consensus. The present study conducted a Bayesian network analysis to investigate and map the interdependencies between factors associated with SB through the life-course from large scale empirical data.
Posted 5 Sep 2019 · Open Link · Link

Modeling interrelationships between health behaviors in overweight breast cancer survivors: Applying Bayesian networks

Xu, Selene AND Thompson, Wesley AND Kerr, Jacqueline AND Godbole, Suneeta AND Sears, Dorothy D. AND Patterson, Ruth AND Natarajan, Loki
Obesity and its impact on health is a multifaceted phenomenon encompassing many factors, including demographics, environment, lifestyle, and psychosocial functioning. A systems science approach, investigating these many influences, is needed to capture the complexity and multidimensionality of obesity prevention to improve health. Leveraging baseline data from a unique clinical cohort comprising 333 postmenopausal overweight or obese breast cancer survivors participating in a weight-loss trial, we applied Bayesian networks, a machine learning approach, to infer interrelationships between lifestyle factors (e.g., sleep, physical activity), body mass index (BMI), and health outcomes (biomarkers and self-reported quality of life metrics). We used bootstrap resampling to assess network stability and accuracy, and Bayesian information criteria (BIC) to compare networks. Our results identified important behavioral subnetworks. BMI was the primary pathway linking behavioral factors to glucose regulation and inflammatory markers; the BMI-biomarker link was reproduced in 100% of resampled networks. Sleep quality was a hub impacting mental quality of life and physical health with > 95% resampling reproducibility. Omission of the BMI or sleep links significantly degraded the fit of the networks. Our findings suggest potential mechanistic pathways and useful intervention targets for future trials. Using our models, we can make quantitative predictions about health impacts that would result from targeted, weight loss and/or sleep improvement interventions. Importantly, this work highlights the utility of Bayesian networks in health behaviors research.
Posted 25 Oct 2018 · Link