<b>deal</b>: A Package for Learning Bayesian Networks

Boettcher, Susanne G.; Dethlefsen, Claus

doi:10.18637/jss.v008.i20

Cited by 88 publications

(102 citation statements)

References 12 publications

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“…The quantitative aspect quantifies the degree of relationships by conditional probability distributions, which configures a Bayesian network model [18,22]. The Bayesian network was constructed using the R package ''deal'' [4]. All eight candidate variables were considered.…”

Section: Bayesian Network Analysismentioning

confidence: 99%

Prediction of Postoperative Clinical Recovery of Drop Foot Attributable to Lumbar Degenerative Diseases, via a Bayesian Network

Takenaka

Aono

2017

Clinical Orthopaedics &Amp; Related Research

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Background Drop foot resulting from degenerative lumbar diseases can impair activities of daily living. Therefore, predictors of recovery of this symptom have been investigated using univariate or/and multivariate analyses. However, the conclusions have been somewhat controversial. Bayesian network models, which are graphic and intuitive to the clinician, may facilitate understanding of the prognosis of drop foot resulting from degenerative lumbar diseases. Questions/purposes (1) To show a layered correlation among predictors of recovery from drop foot resulting from degenerative lumbar diseases; and (2) to develop support tools for clinical decisions to treat drop foot resulting from lumbar degenerative diseases.Methods Between 1993 and 2013, we treated 141 patients with decompressive lumbar spine surgery who presented with drop foot attributable to degenerative diseases. Of those, 102 (72%) were included in this retrospective study because they had drop foot of recent development and had no diseases develop that affect evaluation of drop foot after surgery. Specifically, 28 (20%) patients could not be analyzed because their records were not available at a minimum of 2 years followup after surgery and 11 (8%) were lost owing to postoperative conditions that affect the muscle strength evaluation. Eight candidate variables were sex, age, herniated soft disc, duration of the neurologic injury (duration), preoperative tibialis anterior muscle strength (pretibialis anterior), leg pain, cauda equina syndrome, and number of involved levels. Manual muscle testing was used to assess the tibialis anterior muscle strength. Drop foot was defined as a tibialis anterior muscle strength score of less than 3 of 5 (5 = movement against gravity and full resistance, 4 = movement against gravity and moderate resistance, 3 = movement against gravity through full ROM, 3À = movement against gravity through partial ROM, 2 = movement with gravity eliminated through full ROM, 1 = slight contraction but no movement, and 0 = no contraction). The two outcomes of interest were postoperative tibialis anterior muscle strength (posttibialis anterior) of 3 or greater and posttibialis anterior strength of 4 or greater at 2 years after surgery. We developed two separate Bayesian network models with outcomes of interest for posttibialis anterior strength of 3 or greater and posttibialis anterior strength of 4 or greater. The two outcomes correspond to ''good'' and ''excellent'' results based on previous reports, respectively. Direct predictors are defined as variables that have the tail of the arrow connecting the outcome of interest, whereas indirect predictors are defined as variables that have the tail of the arrow Each author certifies that neither he, nor any member of his immediate family, have funding or commercial associations (consultancies, stock ownership, equity interest, patent/licensing arrangements, etc) that might pose a conflict of interest in connection with the submitted article. All ICMJE Conflict of Interest Forms for ...

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Section: Bayesian Network Analysismentioning

confidence: 99%

Prediction of Postoperative Clinical Recovery of Drop Foot Attributable to Lumbar Degenerative Diseases, via a Bayesian Network

Takenaka

Aono

2017

Clinical Orthopaedics &Amp; Related Research

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“…where a t j pa(t) is a regression intercept, b t j pa(t) is a vector of partial regression coefficients, and r 2 t j pa(t) is the conditional variance (Bøttcher and Dethlefsen 2003).…”

Section: Statistical Analysesmentioning

confidence: 99%

“…a), and the taxonomic richness and community density of the four studied guilds using Bayesian networks (Appendix C). Although several techniques can be used to learn a Bayesian network from particular data and to detect the network that gives the best representation of the data (Bøttcher andDethlefsen 2003, Scutari 2010), we used a greedy search based on the Bayes factor and an uninformative prior distribution for the joint probability (Appendix C). Subsequently, standardized partial regression coefficients (SPRC) were calculated to assess the relative importance of each explanatory variable in the best representing network by multiplying the partial regression coefficient by the standard deviation of its explanatory variable and dividing it by the standard deviation of the response variable (Sokal and Rohlf 1995).…”

Section: Statistical Analysesmentioning

confidence: 99%

Flood disturbance and predator–prey effects on regional gradients in species diversity

Mori

Saitoh

2014

Ecology

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Abstract. The effects of both abiotic factors and biotic interactions among guilds (i.e., inter-guild effects) have been suggested to be important for understanding spatial variation in species diversity; however, compared to the abiotic effects, the processes by which the interguild effects are mediated have been little described. Hence, we investigated stream invertebrate assemblages on Hokkaido Island, Japan, and assessed how the processes of determining regional patterns in species diversity differed among guilds (collector-filterers, collectorgatherers/shredders, scrapers, and predators) by taking both inter-guild and abiotic effects into consideration using Bayesian networks. Collector-gatherers/shredders, collector-filterers, and predators exhibited significant regional gradients in taxonomic richness. Gradients in the former two guilds can be generated by variation in flood disturbance regardless of interactions with other guilds. The gradient in predator taxonomic richness was indirectly related to the disturbance and was directly generated by bottom-up effects through their prey (collectorgatherers/shredders and collector-filterers). We found that not only environmental factors, but also inter-guild effects may be essential for forming the regional gradient in predators, unlike those for collector-gatherers/shredders and collector-filterers. The processes underlying the regional variation in taxonomic richness of the three guilds are interpreted in terms of the ''more individuals'' hypothesis, facilitation, and predator-prey relationships.

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“…Gibbs N (Conrady and Jouffe, 2011) BNT (Aliferis, et al, 2003) Deal (Bøttcher and Dethlefsen, 2003) Genie (Druzdzel, 1999) Java Bayes Java miniTUBA openBUGS (Lunn, et al, 2000;McCarthy, 2007) OpenPNL (Shah and Woolf, 2009) WinMine (Chickering, 2002) Notes: The catergories listed include: Source, source code; API, application program interface for programmatic access; GUI, graphical user interface; Undir, ability to handle undirected graphes; Exec, the type of execution, including W:Windows, U:Unix, L:Linux, M:Mac, I:OS-independent, or C:any with compiler; Free, the availability of the software as either free (e.g. academic) or commercial; Inference, inferencing ability; Exp, ability for network expansion; and Ref, references.…”

Section: Bayesian Network Analysis Softwarementioning

confidence: 99%