Diabetes Bayesian Network with 413 Nodes and 602 Arcs

413 nodes and 602 arcs define this discrete Bayesian network for modeling diabetes. The network, authored by S. Andreassen, R. Hovorka, J. Benn, K. G. Olesen, and E. R. Carson, contains 429,409 parameters and was presented at the 3rd Conference on Artificial Intelligence in Medicine in 1991.

Use Cases

• Probabilistic inference for diabetes-related variables based on the network's 413 nodes
• Modeling causal relationships in diabetes progression based on the 602 arcs
• Benchmarking structure learning algorithms based on the known network topology
• Simulating patient states for insulin adjustment strategies based on the model's parameters

Strengths

• Large-scale structure with 413 nodes and 602 arcs
• Well-defined probabilistic model with 429,409 parameters
• Clear academic provenance from a published 1991 conference paper

Limitations

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

Provenance

Source

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

Collection Method

Model-based approach presented in academic research

Time Range

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Freshness

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Geography

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