2018
DOI: 10.1371/journal.pone.0201355
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A network perspective of engaging patients in specialist and chronic illness care: The 2014 International Health Policy Survey

Abstract: BackgroundPatient engagement helps to improve health outcomes and health care quality. However, the overall relationships among patient engagement measures and health outcomes remain unclear. This study aims to integrate expert knowledge and survey data for the identification of measures that have extensive associations with other variables and can be prioritized to engage patients.MethodsWe used the 2014 International Health Policy Survey (IHPS), which provided information on elder adults in 11 countries with… Show more

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Cited by 7 publications
(9 citation statements)
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“…BN modeling enables simultaneous exploration of interrelationships among a large number of diseases and their related risk factors, without affecting the interpretability of the network ( 43 , 49 ). Another advantage of BN modeling is that, thanks to Markov blanket theory, complex models can be divided into a collection of simpler models that which are mathematically tractable and computationally simpler ( 71 ).…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…BN modeling enables simultaneous exploration of interrelationships among a large number of diseases and their related risk factors, without affecting the interpretability of the network ( 43 , 49 ). Another advantage of BN modeling is that, thanks to Markov blanket theory, complex models can be divided into a collection of simpler models that which are mathematically tractable and computationally simpler ( 71 ).…”
Section: Discussionmentioning
confidence: 99%
“…BNs were constructed from the data by the following steps: (1) apply one of the most commonly used heuristic algorithms, Tabu ( 47 ), for graphical structure learning along with the Bayesian Information Criterion score ( 48 ); (2) examine the stabilities of arcs in the networks from averaging 300 bootstrapped networks, and estimate the strengths of arcs by averaging the probability of the arcs presenting in the bootstrapped networks ( 49 ); (3) determine the structure and directions of arcs between variables using the averaged network; (4) query the conditional probability distributions (Bayesian reasoning) in the final networks and (5) visualize the final networks ( 50 ).…”
Section: Methodsmentioning
confidence: 99%
“…In principle, such causal graphs can be used to answer questions related to: (i) causal discovery (e.g., which symptom(s), among many co-occurring symptoms, can influence another distinct symptom) and (ii) causal inference (e.g., of what magnitude and causal connection -direct or confounding -these symptoms affect the aforementioned sentinel or core symptom). 17 Because of BNA's ability to handle both observable as well as uncertain information (i.e., latent factors from observational data), [18][19][20] BNA has been used in biomedical informatics for more than two decades.…”
Section: Introductionmentioning
confidence: 99%
“…BNs are suitable tools for the probabilistic inference that can aid in clinical decision making for several reasons. 18,[21][22][23] For example, they provide a visual representation of causality that can be easily understood by a clinician. 22,24,25 They can integrate knowledge from clinical experts into their modelling procedures while learning the structure and parameters of a network through the available data.…”
Section: Introductionmentioning
confidence: 99%
“…This model can be used for intelligent information processing such as probabilistic causation of clinical parameters and profile of cell signaling including uncertainty by using probability calculation. Recently, Bayesian network analysis is applied for clinical practice with electric health record [1,2] and intricate hierarchical analysis [3]. Bayesian network analysis in studies of a single disease such as Alzheimer's disease [4] and hip fracture [5] facilitates the probability causation and hierarchical structure.…”
Section: Introductionmentioning
confidence: 99%