These are simulated data on 10,000 cases with suspected pulmonary embolism at a hospital.

datasets

Facilities for easy implementation of hybrid Bayesian networks
using R. Bayesian networks are directed acyclic graphs representing joint
probability distributions, where each node represents a random variable and
each edge represents conditionality. The full joint distribution is therefore
factorized as a product of conditional densities, where each node is assumed
to be independent of its non-descendents given information on its parent nodes.
Since exact, closed-form algorithms are computationally burdensome for inference
within hybrid networks that contain a combination of continuous and discrete
nodes, particle-based approximation techniques like Markov Chain Monte Carlo
are popular. We provide a user-friendly interface to constructing these networks
and running inference using the 'rjags' package. Econometric analyses (maximum
expected utility under competing policies, value of information) involving
decision and utility nodes are also supported.

Benjamin Nutter

HydeNet

Hybrid Bayesian Networks Using R and JAGS

PE function

A data frame with 10000 rows and 7 variables:<dl class="dl-horizontal">
 <dt>wells</dt><dd>Wells score (integer ranging from 1 to 10 indicating the degree to which PE is suspected based on clinical review of symptoms)</dd>
 <dt>pregnant</dt><dd>Factor indicating pregnancy (No, Yes)</dd>
 <dt>pe</dt><dd>Factor indicating pulmonary embolism has occurred (No,Yes)</dd>
 <dt>angio</dt><dd>Result of pulmonary angiography test (Negative, Positive)</dd>
 <dt>d.dimer</dt><dd>Numeric result of diagnostic blood test called D-Dimer.</dd>
 <dt>treat</dt><dd>Factor indicating whether or not treatment for PE was administered (No,Yes)</dd>
 <dt>death</dt><dd>Factor indicating patient mortality (No,Yes)</dd>
</dl>

Format

Pulmonary Embolism Dataset — PE

A data frame with 10000 rows and 7 variables:<dl class='dl-horizontal'>
 <dt>wells</dt><dd>Wells score (integer ranging from 1 to 10 indicating the degree to which PE is suspected based on clinical review of symptoms)</dd>
 <dt>pregnant</dt><dd>Factor indicating pregnancy (No, Yes)</dd>
 <dt>pe</dt><dd>Factor indicating pulmonary embolism has occurred (No,Yes)</dd>
 <dt>angio</dt><dd>Result of pulmonary angiography test (Negative, Positive)</dd>
 <dt>d.dimer</dt><dd>Numeric result of diagnostic blood test called D-Dimer.</dd>
 <dt>treat</dt><dd>Factor indicating whether or not treatment for PE was administered (No,Yes)</dd>
 <dt>death</dt><dd>Factor indicating patient mortality (No,Yes)</dd>
</dl>

PE: Pulmonary Embolism Dataset

Description

Usage

Arguments

Format