Diabetes Bayesian Network with 413 Nodes and 602 Arcs

413 nodes and 602 arcs form a Bayesian network for modeling diabetes. The network was authored by S. Andreassen, R. Hovorka, J. Benn, K. G. Olesen, and E. R. Carson and referenced in a 1991 conference paper on a model-based approach to insulin adjustment.

Use Cases

• Probabilistic inference for diabetes-related variables based on the network's 429,409 parameters.
• Modeling causal relationships in diabetes pathophysiology based on the described Bayesian network structure.
• Benchmarking structure learning algorithms based on the network's 413 nodes and 602 arcs.
• Developing clinical decision aids for insulin adjustment based on the model referenced in the description.

Strengths

• The network structure is explicitly defined with 413 nodes and 602 arcs.
• The model has a large parameter space of 429,409 parameters for detailed probabilistic representation.
• The network has a well-defined authorship and academic citation from a peer-reviewed conference.

Limitations

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

Provenance

Source

S. Andreassen, R. Hovorka, J. Benn, K. G. Olesen, and E. R. Carson.

Collection Method

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Time Range

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Freshness

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Geography

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