Diabetes Bayesian Network with 413 Nodes and 602 Arcs

A Bayesian network model for diabetes, referenced from the bnlearn repository. The model contains 413 nodes, 602 arcs, and 429,409 parameters, with an average Markov blanket size of 3.97. It was authored by S. Andreassen, R. Hovorka, J. Benn, K. G. Olesen, and E. R. Carson for a model-based approach to insulin adjustment, presented in 1991.

Use Cases

• Studying causal relationships in diabetes pathophysiology based on the network structure.
• Developing probabilistic inference tools for clinical decision support based on the model's parameters.
• Benchmarking structure learning algorithms against a known large-scale medical network.
• Simulating patient scenarios for insulin adjustment strategies based on the model's logic.

Strengths

• The network structure is explicitly defined with 413 nodes and 602 arcs.
• Model complexity is quantified with 429,409 parameters.
• Clear authorship and citation to a peer-reviewed 1991 conference paper.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

bnlearn Bayesian Network Repository, originally from a 1991 conference paper by Andreassen et al.

Collection Method

Model-based approach for insulin adjustment, likely derived from expert knowledge and data.

Time Range

Model presented in 1991; underlying data time range is unknown.

Freshness

Last updated date is unknown.

Geography

Geographic coverage is unknown.