Bayesian Network Modeling of Environmental, Social, and Behavioral Determinants of Cardiovascular Disease Risk. [PDF]
Nyavor H, Obeng-Gyasi E.
europepmc +1 more source
Abstract Background Parenteral nutrition–associated liver disease is a severe complication in very low birth weight (≤1500 g) preterm neonates. Although previous pairwise meta‐analyses have compared individual intravenous lipid emulsions, their relative efficacy across all available formulations has not been established in this population.
Maria Di Chiara +2 more
wiley +1 more source
Relationship between insulin resistance surrogate markers with diabetes and dyslipidemia: A Bayesian network analysis of Korean adults. [PDF]
Choi J, Kim J, Oh HS.
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Abstract The Momentary Posttraumatic Stress Disorder Checklist for DSM‐5 (PTSD; mPCL‐5) is a 10‐item ecological momentary assessment (EMA) version of the PTSD Checklist for DSM‐5 designed to capture momentary PTSD symptoms. Prior work supports its reliability and validity but is limited by small samples, short EMA periods, and untested stability across
Grace J. Patrick +5 more
wiley +1 more source
Cognitive fatigue from schema rigidity and entropy externalization: a free energy principle perspective. [PDF]
Ezzedini S.
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We illustrate how a Bayesian multi‐state mark‐resight (MSMR) modeling approach can be used to estimate survival and cause‐specific mortality probabilities of GPS‐collared European fallow deer in southeastern Australia. Our case study illustrates the flexibility of Bayesian MSMR models for incorporating imperfect detection probability (arising from ...
David M. Forsyth +6 more
wiley +1 more source
Leveraging Deep Learning, Grid Search, and Bayesian Networks to Predict Distant Recurrence of Breast Cancer. [PDF]
Jiang X, Zhou Y, Wells A, Brufsky A.
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Integrating Machine Learning and Dynamic Bayesian Networks to Identify the Factors Associated with Subsequent Intrapulmonary Metastasis Classification After Initial Single Primary Lung Cancer. [PDF]
Liu W +6 more
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Fast Estimation and Valid Statistical Inference for Mixed-Effect Location-Scale Models Using Variational Inference. [PDF]
Wu BP, Hedeker D.
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Sensitivity of Bayesian Networks to Noise in Their Parameters. [PDF]
Onisko A, Druzdzel MJ.
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